Peter Stone's Selected Publications

• Classified by Topic • Classified by Publication Type • Sorted by Date • Sorted by First Author Last Name • Classified by Funding Source •

Classified by Topic

• Artificial Intelligence • AI Science • Autonomic Computing • Autonomous Traffic Management • Computational Epidemiology • Game Theory • General Game Playing • Machine Learning • Machine Learning: Layered Learning • Machine Learning: Markov Decision Processes • Machine Learning: NeuroEvolution • Machine Learning: Predictive State Representations • Machine Learning: Reinforcement Learning • Machine Learning: Transfer Learning • Machine Learning: Imitation Learning • Machine Learning: Learning from Humans • Machine Learning: Lifelong Learning • Multi-Robot Systems • Multiagent Systems • Multiagent Systems: Ad Hoc Teamwork • Multiagent Systems: Agent Modeling • Multiagent Systems: Social Agents • Planning • Robotics • Robotics: Autonomous Driving • Robotics: Quadruped Robots • Robotics: Humanoid Robots • Robotics: Real Robot Soccer • Robotics: RoboCup • Robotics: Simulated Robot Soccer • Robotics: Vision • Robotics: Service Robots • Robotics: Human-Robot Interaction • Trading Agents • Trading Agents: Auctions • Trading Agents: Mechanism Design • Trading Agents: Supply Chain Management • Unspecified •

Artificial Intelligence

• The Einstein Test: A Test of AI's Ability to Generate Transformative Science.
  David Benrimoh, David Harel, Nace Mikus, Peter Stone, and Ariel Rosenfeld.
  Communications of the ACM, 69(5):49–50, Association for Computing Machinery, New York, NY, USA, May 2026.
  Official version on publisher's website
  Details     BibTeX     Download: (unavailable)

• Hakken: Predicting future discoveries to fill the gaps in today's knowledge.
  Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez, Alessandra Toniato, Kana Maruyama, Jihun Choi, Samy Badreddine, Frederick Gifford, Daniel Evans-Yamamoto, Sucheendra K. Palaniappan, Miquel Ferrer, Kae Nagano, Iris Rossell, Tom Joy, Hatem ElShazly, Chrysa Iliopoulou, Christoph Wehner, Thiviyan Thanapalasingam, Susana Nunes, Pedro G. Cotovio, Peter Wurman, Peter Stone, Hiroaki Kitano, and Michael Spranger.
  Technical Report arXiv e-Prints 2609.04494, arXiv, 2026.
  arXiv version
  Details     BibTeX     Download: (unavailable)

• Coachable agents for interactive gameplay.
  Roberto Capobianco, Harm van Seijen, Nolan D. Bard, Neil Burch, Fatima Davelouis, Josh Davidson, Alisa Devlic, Yunshu Du, Ishan Durugkar, Siddhant Gangapurwala, Daniel Hernandez, G. Zacharias Holland, Sahil Jain, Kenta Kawamoto, Raksha Kumaraswamy, Patrick MacAlpine, Dustin R. Morrill, Declan Oller, Francesco Riccio, Akanksha Saran, Craig Sherstan, Kaushik Subramanian, Thomas J. Walsh, Samuel Barrett, Kizza N. Frisbee, Mady Govil, Johannes Günther, Varun R. Kompella, James A. MacGlashan, Maxwell Svetlik, Michael D. Thomure, Jaden B. Travnik, Kevin Waugh, Elahe Aghapour, Florian Fuchs, Andreanne Lemay, Shruti Mishra, Takuma Seno, Peter Stone, Michael Spranger, and Peter R. Wurman.
  Technical Report arXiv e-Prints 2607.00642, arXiv, 2026.
  arXiv version
  Details     BibTeX     Download: [pdf] (9.1MB )  

• CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning.
  Jiaxun Cui, Chen Tang, Jarrett Holtz, Janice Nguyen, Alessandro G Allievi, Hang Qiu, and Peter Stone.
  In International Conference on Autonomous Agents and Multi-Agent Systems, May 2026.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Baby Mesh Recovery Enables Infant Behavior Identification.
  Charles Dardaman, Zifan Xu, Peter Stone, Karen Adolph, Danyang Han, and Georgios Pavlakos.
  In The IEEE International Conference on Development and Learning (ICDL), September 2026.
  Details     BibTeX     Download: [pdf] (8.0MB )  

• Outplaying Elite Table Tennis Players with an Autonomous Robot.
  Peter Duerr, Mireille El Gheche, Guilherme Jorge Maeda, Nobuhiko Mukai, Naoya Takahashi, Stefan Heusser, Hamdi Sahloul, Yamen Saraiji, Pavel Adodin, Yin Bi, Sam Blakeman, Christian Conti, Dunai Fuentes Hitos, Yunpu Hu, Farshad Khadivar, Raphaela Kreiser, Luz Martinez, Fabian Schilling, Ricardo Tapiador Morales, Guillem Torrente, Mario Ynocente Castro, Lison Abecassis, Alberto Giammarino, Yu-Ting Huang, Yannik Nagel, Andrea Scotti, Alexander Sigrist, Tiago Silva, Etienne Walther, Jengyan Wong, Bilan Yang, Asude Aydin, Divij Grover, Apurv Saha, Valentina Cavinato, Takekazu Kakinuma, Taishi Kunori, Valentin Monferrato, Stefan Richter, Stefanos Charalambous, Simon Guist, MadsAlber Kuhlmann-Jorgensen, Lorenzo Miele, Agis Politis, Mattia Scardecchia, Hiroaki Kitano, Peter R. Wurman, Peter Stone, and Michael Spranger.
  Nature, 652:886–91, 2026.
  The official paper from Nature
  The supplementary material
  A video about the project
  The project website
  Details     BibTeX     Download: (unavailable)

• LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks.
  Faraz Heravi, James Ouyang, Zifan Xu, Arjun Kumar, Yoonchang Sung, and Peter Stone.
  In The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2026.
  Details     BibTeX     Download: [pdf] (5.9MB )  

• Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning.
  Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, and Roberto Martin-Martin.
  Reinforcement Learning Journal, 7, August 2026.
  Official version
  Details     BibTeX     Download: [pdf] (7.9MB )  

• VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids.
  Zichao Hu, Zifan Xu, Dongsik Chang, He Yin, Linh Tran, Roberto Martın-Martın, Peter Stone, Jingyu Qiao, and Joydeep Biswas.
  In The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2026.
  Details     BibTeX     Download: [pdf] (2.3MB )  

• Large-Language-Model-Guided State Estimation for Partially Observable Task and Motion Planning.
  Yoonwoo Kim, Raghav Arora, Roberto Martín-Martín, Peter Stone, Ben Abbatematteo, and Yoonchang Sung.
  In International Conference on Robotics and Automation, June 2026.
  Details     BibTeX     Download: [pdf] (2.9MB )  [slides.pdf] (1.6MB )  [poster.pdf] (1.3MB )  

• The Essentials of AI for Life and Society: A Full-Scale AI Literacy Course Accessible to All.
  Zifan Xu, Kristen Procko, Michael Munje, Kristin Patterson, Lea Sabatini, Joydeep Biswas, and Peter Stone.
  In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 40730–6, January 2026.
  Official published version
  Appeared in Symposium on Educational Advances in Artificial Intelligence
  Course webpage
  Details     BibTeX     Download: [pdf] (954.6kB )  

• RoboSSM: Scalable In-Context Imitation Learning Via State-Space Models.
  Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Martín-Martín, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2026.
  Details     BibTeX     Download: [pdf] (2.8MB )  

• The Essentials of AI for Life and Society: An AI Literacy Course for the University Community.
  Joydeep Biswas, Don Fussell, Peter Stone, Kristin Patterson, Kristen Procko, Lea Sabatini, and Zifan Xu.
  In Proceedings of the AAAI Conference on Artificial Intelligence, pp. 28973–8, April 2025.
  Official published version
  Appeared in Symposium on Educational Advances in Artificial Intelligence
  Course webpage
  Details     BibTeX     Download: [pdf] (262.3kB )  [slides.pdf] (1.7MB )  

• MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention.
  Yuxin Chen, Chen Tang, Jianglan Wei, Chenran Li, Ran Tian, Xiang Zhang, Wei Zhan, Peter Stone, and Masayoshi Tomizuka.
  In Conference on Robot Learning (CoRL), September 2025.
  Details     BibTeX     Download: [pdf] (18.9MB )  

• ComposableNav: Instruction-Following Navigation in Dynamic Environments via Composable Diffusion.
  Zichao Hu, Chen Tang, Michael J. Munje, Yifeng Zhu, Alex Liu, Shuijing Liu, Garrett Warnell, Peter Stone, and Joydeep Biswas.
  In Conference on Robot Learning, September 2025.
  Details     BibTeX     Download: [pdf] (12.0MB )  

• Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks.
  Viraj Joshi, Zifan Xu, Bo Liu, Peter Stone, and Amy Zhang.
  Reinforcement Learning Journal, 6:1293–317, August 2025.
  Official version
  Details     BibTeX     Download: [pdf] (3.5MB )  

• Harmful Traits of AI Companions.
  W. Bradley Knox, Katie Bradford, Samanta Varela Castro, Desmond C. Ong, Sean Williams, Jacob Romanow, Carly Nations, Peter Stone, and Samuel Baker.
  arXiv preprint arXiv:2511.14972, 2025.
  arXiv
  a blog post about the paper
  Details     BibTeX     Download: [pdf] (1.4MB )  

• Reinforcement Learning within the Classical Robotics Stack: A Case Study in Robot Soccer.
  Adam Labiosa, Zhihan Wang, Siddhant Agarwal, William Cong, Geethika Hemkumar, Abhinav Narayan Harish, Benjamin Hong, Josh Kelle, Chen Li, Yuhao Li, Zisen Shao, Peter Stone, and Josiah Hanna.
  In International Conference on Robotics and Automation (ICRA), May 2025.
  Details     BibTeX     Download: [pdf] (1.1MB )  

• SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation.
  Michael J. Munje, Chen Tang, Shuijing Liu, Zichao Hu, Yifeng Zhu, Jiaxun Cui, Garrett Warnell, Joydeep Biswas, and Peter Stone.
  In Conference on Robot Learning, September 2025.
  Details     BibTeX     Download: [pdf] (22.5MB )  

• PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation.
  Mingyo Seo, Yoonyoung Cho, Yoonchang Sung, Peter Stone, Yuke Zhu, and Beomjoon Kim.
  In IEEE International Conference on Robotics and Automation (ICRA), May 2025.
  Details     BibTeX     Download: [pdf] (1.4MB )  [slides.pdf] (2.1MB )  [poster.pdf] (1.3MB )  

• Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia.
  Chandler Smith, Marwa Abdulhai, Manfred Diaz, Marko Tesic, Rakshit S. Trivedi, Alexander Sasha Vezhnevets, Lewis Hammond, Jesse Clifton, Minsuk Chang, Edgar A. Duenez-Guzmán, John P. Agapiou, Jayd Matyas, Danny Karmon, Akash Kundu, Aliaksei Korshuk, Ananya Ananya, Arrasy Rahman, Avinaash Anand Kulandaivel, Bain McHale, Beining Zhang, Buyantuev Alexander, Carlos Saith Rodriguez Rojas, Caroline Wang, Chetan Talele, Chenao Liu, Chichen Lin, Diana Riazi, Di Yang Shi, Emanuel Tewolde, Elizaveta Tennant, Fangwei Zhong, Fuyang Cui, Gang Zhao, Gema Parreno Piqueras, Hyeonggeun Yun, Ilya Makarov, Jiaxun Cui, Jebish Purbey, Jim Dilkes, Jord Nguyen, Lingyun Xiao, Luis Felipe Giraldo, Manuela Chacon-Chamorro, Manuel Sebastian Rios Beltran, Marta Emili Garcia Segura, Mengmeng Wang, Mogtaba Alim, Nicanor Quijano, Nico Schiavone, Olivia Macmillan-Scott, Oswaldo Pena, Peter Stone, Ram Mohan Rao Kadiyala, Rolando Fernandez, Ruben Manrique, Sunjia Lu, Sheila A. McIlraith, Shamika Dhuri, Shuqing Shi, Siddhant Gupta, Sneheel Sarangi, Sriram Ganapathi Subramanian, Taehun Cha, Toryn Q. Klassen, Wenming Tu, Weijian Fan, Wu Ruiyang, Xue Feng, Yali Du, Yang Liu, Yiding Wang, Yipeng Kang, Yoonchang Sung, Yuxuan Chen, Zhaowei Zhang, Zhihan Wang, Zhiqiang Wu, Ziang Chen, Zilong Zheng, Zixia Jia, Ziyan Wang, Dylan Hadfield-Menell, Natasha Jaques, Tim Baarslag, Jose Hernandez-Orallo, and Joel Z. Leibo.
  In Conference on Neural Information Processing Systems (NeurIPS, September 2025.
  Details     BibTeX     Download: [pdf] (4.1MB )  

• Artificial Intelligence: Looking Forward 15 Years.
  Peter Stone.
  IEEE Intelligent Systems, 2025.
  Details     BibTeX     Download: [pdf] (517.0kB )  

• Fair human-centric image dataset for ethical AI benchmarking.
  Alice Xiang, Jerone T. A. Andrews, Rebecca L. Bourke, William Thong, Julienne M. LaChance, Tiffany Georgievski, Apostolos Modas, Aida Rahmattalabbi, Yunhao Ba, Shruti Nagpal, Orestis Papakyriakopoulos, Dora Zhao, Jinru Xue, Victoria Matthews, Linxia Gong, Austin T. Hoag, Mircea Cimpoi, Swami Sankaranarayanan, Wiebke Hutiri, Morgan K. Scheuerman, Albert S. Abedi, Peter Stone, Peter R. Wurman, Hiroaki Kitano, and Michael Spranger .
  Nature, 648:97–108, 2025.
  The official paper from Nature
  An editorial about the paper
  The project website
  Details     BibTeX     Download: [pdf] (43.9MB )  

• Argus: A Compact and Versatile Foundation Model for Vision.
  Weiming Zhuang, Chen Chen, Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Jiabo Huang, Vikash Sehwag, Vivek Sharma, Hirotaka Shinozaki, Felan Carlo Garcia, Yihao Zhan, Naohiro Adachi, Ryoji Eki, Michael Spranger, Peter Stone, and Lingjuan Lyu.
  In Conference on Computer Vision and Pattern Recognition, June 2025.
  Details     BibTeX     Download: [pdf] (3.1MB )  

• Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference-Aligned Path Planning.
  Haresh, Karnan; Elvin, Yang; Garrett, Warnell; Joydeep, Biswas; Peter, and Stone.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (4.3MB )  

• Learning Optimal Advantage from Preferences and Mistaking it for Reward.
  W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson, Serena Booth, Anca Dragan, Peter Stone, and Scott Niekum.
  In The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), February 2024.
  Details     BibTeX     Download: [pdf] (3.6MB )  [slides.pdf] (3.9MB )  [poster.pdf] (2.9MB )  

• Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents.
  Arrasy Rahman, Jiaxun Cui, and Peter Stone.
  In AAAI, February 2024.
  the conference presentation
  Details     BibTeX     Download: [pdf] (1.3MB )  [slides.ppt] (9.2MB )  [poster.pdf] (956.2kB )  

• Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds.
  Amir Hossain Raj, Zichao Hu, Haresh Karnan, Rohan Chandra, Amirreza Payandeh, Luisa Mao, Peter Stone, Joydeep Biswas, and and Xuesu Xiao.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• Dobby: A Conversational Service Robot Driven by GPT-4.
  Carson Stark, Bohkyung Chun, Casey Charleston, Varsha Ravi, Luis Pabon, Surya Sunkari, Tarun Mohan, Peter Stone, and Justin Hart.
  In International Symposium on Robot and Human Interactive Communication (RO-MAN), January 2024.
  Details     BibTeX     Download: [pdf] (863.8kB )  [poster.pdf] (892.0kB )  

• A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo.
  Miguel Vasco, Takuma Seno, Kenta Kawamoto, Kaushik Subramanian, Peter R. Wurman, and Peter Stone.
  Reinforcement Learning Journal, 4:1674–710, August 2024.
  Official version
  arXiv version
  Details     BibTeX     Download: [pdf] (15.4MB )  

• N-Agent Ad Hoc Teamwork.
  Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman, and Peter Stone.
  In Conference on Neural Information Processing Systems (NeurIPS), December 2024.
  Details     BibTeX     Download: [pdf] (1.6MB )  [slides.pdf] (1.3MB )  [poster.pdf] (1.7MB )  

• Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning.
  Zizhao Wang, Caroline Wang, Xuesu Xiao, Yuke Zhu, and Peter Stone.
  In AAAI Conference on Artificial Intelligence, February 2024.
  Details     BibTeX     Download: [pdf] (4.5MB )  

• Real-time Trajectory Generation via Dynamic Movement Primitives for Autonomous Racing.
  Catherine Weaver, Roberto Capobianco, Peter Wurman, Peter Stone, and Masayoshi Tomizuka.
  In American Control Conference (ACC), July 2024.
  Project page with video.
  Details     BibTeX     Download: [pdf] (1.5MB )  

• LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning.
  Zifan Xu, Haozhu Wang, Dmitriy Bespalov, Xian Wu, Peter Stone, and Yanjun Qi.
  In Findings of Empirical Methods in Natural Language Processing, November 2024.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making.
  William Yue, Bo Liu, and Peter Stone.
  In Conference on Lifelong Learning Agents (CoLLAs), July 2024.
  Details     BibTeX     Download: [pdf] (599.7kB )  [poster.pdf] (709.1kB )  

• Models of human preference for learning reward functions.
  W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, and Alessandro Allievi.
  Transactions on Machine Learning Research (TMLR), 2023.
  Details     BibTeX     Download: [pdf] (6.7MB )  [slides.pdf] (13.4MB )  

• LIBERO: Benchmarking Knowledge Transfer in Lifelong Robot Learning.
  Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone.
  In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks, December 2023.
  Details     BibTeX     Download: [pdf] (37.6MB )  [poster.pdf] (2.6MB )  

• Improving Artificial Intelligence with Games.
  Peter R. Wurman, Peter Stone, and Michael Spranger.
  Science, 381:147–8, July 2023.
  Available from Science website.
  Details     BibTeX     Download: (unavailable)

• Learning a robust multiagent driving policy for traffic congestion reduction.
  Yulin Zhang, William Macke, Jiaxun Cui, Sharon Hornstein, Daniel Urieli, and Peter Stone.
  Neural Computing and Applications, 2023.
  Details     BibTeX     Download: [pdf] (1.2MB )  

• A Broader, More Inclusive Definition of AI.
  Peter Stone.
  Journal of Artificial General Intelligence, 11(2):63–65, 2020.
  Details     BibTeX     Download: [pdf] (50.6kB )  

AI Science

• Hakken: Predicting future discoveries to fill the gaps in today's knowledge.
  Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez, Alessandra Toniato, Kana Maruyama, Jihun Choi, Samy Badreddine, Frederick Gifford, Daniel Evans-Yamamoto, Sucheendra K. Palaniappan, Miquel Ferrer, Kae Nagano, Iris Rossell, Tom Joy, Hatem ElShazly, Chrysa Iliopoulou, Christoph Wehner, Thiviyan Thanapalasingam, Susana Nunes, Pedro G. Cotovio, Peter Wurman, Peter Stone, Hiroaki Kitano, and Michael Spranger.
  Technical Report arXiv e-Prints 2609.04494, arXiv, 2026.
  arXiv version
  Details     BibTeX     Download: (unavailable)

Autonomic Computing

• Autonomous Return on Investment Analysis of Additional Processing Resources.
  Jonathan Wildstrom, Peter Stone, and Emmett Witchel.
  International Journal on Autonomic Computing, 1(3):280–296, Inderscience Publishers, Inderscience Publishers, Geneva, SWITZERLAND, 2010.
  IJAC
  Details     BibTeX     Download: (unavailable)

• Machine Learning for On-Line Hardware Reconfiguration.
  Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 1113–1118, January 2007.
  IJCAI-07
  Details     BibTeX     Download: [pdf] (98.6kB )  [ps] (191.7kB )  

• Adapting to Workload Changes Through On-The-Fly Reconfiguration.
  Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin.
  Technical Report UT-AI-TR-06-330, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2006.
  At ftp://ftp.cs.utexas.edu/pub/AI-Lab/tech-reports/UT-AI-TR-06-330.pdf
  Details     BibTeX     Download: (unavailable)

• Towards Self-Configuring Hardware for Distributed Computer Systems.
  Jonathan Wildstrom, Peter Stone, Emmett Witchel, Raymond J. Mooney, and Mike Dahlin.
  In The Second International Conference on Autonomic Computing, pp. 241–249, June 2005.
  ICAC-05
  A revised version of the paper appeared on IBM's Developer Works website
  Details     BibTeX     Download: [pdf] (74.0kB )  [ps] (104.6kB )  

• Adaptive Job Routing and Scheduling.
  Shimon Whiteson and Peter Stone.
  Engineering Applications of Artificial Intelligence, 17(7):855–69, October 2004. Special issue on Autonomic Computing and Automation
  Available from the publisher's webpage
  The version from this page corrects a minor error in the published version.
  An earlier version appeared in the proceedings of The Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI 2004)
  Details     BibTeX     Download: [pdf] (664.7kB )  [ps] (1.1MB )  

Autonomous Traffic Management

• Scalable Multiagent Driving Policies For Reducing Traffic Congestion.
  Jiaxun Cui, William Macke, Harel Yedidsion, Aastha Goyal, Daniel Urieli, and Peter Stone.
  In Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2021.
  Project page, with videos
  Details     BibTeX     Download: [pdf] (1.4MB )  [slides.pptx] (1.5MB )  

• Selecting Compliant Agents for Opt-in Micro-Tolling.
  Josiah Hanna, Guni Sharon, Stephen Boyles, and Peter Stone.
  In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), January 2019.
  Details     BibTeX     Download: [pdf] (2.2MB )  

• Marginal Cost Pricing with a Fixed Error Factor in Traffic Networks.
  Guni Sharon, Stephen D. Boyles, Shani Alkoby, and Peter Stone.
  In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19), May 2019.
  Details     BibTeX     Download: [pdf] (1.7MB )  [slides.pptx] (6.5MB )  

• DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation.
  Haipeng Chen, Bo An, Guni Sharon, Josiah P. Hanna, Peter Stone, Chunyan Miao, and Yeng Chai Soh.
  In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), February 2018.
  Details     BibTeX     Download: [pdf] (2.4MB )  [ps] (5.9MB )  

• Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas --- Final Report.
  Kara Kockelman, Stephen Boyles, Purser Sturgeon, Christian Claudel, Lisa Loftus-Otway, Wendy Wagner, Duncan Stewart, Guni Sharon, Michael Albert, Peter Stone, Josiah Hanna, Yantao Huang, Krishna Murthy Gurumurthy, Dongxu He, Abduallah Mohamed, Rahul Patel, Tian Lei, Michele Simoni, and Sadegh Yarmohammadisatri.
  Technical Report 0-6838-3, The University of Texas at Austin Center for Transportation Research, 2018.
  Available online
  Details     BibTeX     Download: (unavailable)

• Marginal Cost Pricing for System Optimal Traffic Assignment with Recourse under Supply-Side Uncertainty.
  Tarun Rambha, Stephen D. Boyles, Avinash Unnikrishnan, and Peter Stone.
  Transportation Research Part B: Methodological, 110:104–21, 2018.
  Official version from Publisher's Webpage
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Traffic Optimization For a Mixture of Self-interested and Compliant Agents.
  Guni Sharon, Michael Albert, Tarun Rambha, Stephen Boyles, and Peter Stone.
  In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), February 2018.
  Details     BibTeX     Download: [pdf] (1002.7kB )  [ps] (5.2MB )  [slides.pptx] (11.1MB )  

• An Assessment of Autonomous Vehicles: Traffic Impacts and Infrastructure Needs --- Final Report.
  Kara Kockelman, Stephen Boyles, Peter Stone, Dan Fagnant, Rahul Patel, Michael W. Levin, Guni Sharon, Michele Simoni, Michael Albert, Hagen Fritz, Rebecca Hutchinson, Prateek Bansal, Gelb Domnenko, Pavle Bujanovic, Bumsik Kim, Elaham Pourrahmani, Sudesh Agrawal, Tianxin Li, Josiah Hanna, Aqshems Nichols, and Jia Li.
  Technical Report 0-6847-1, The University of Texas at Austin Center for Transportation Research, 2017.
  Available online
  Details     BibTeX     Download: (unavailable)

• Network-wide Adaptive Tolling for Connected and Automated vehicles.
  Guni Sharon, Michael W. Levin, Josiah P. Hanna, Tarun Rambha, Stephen D. Boyles, and Peter Stone.
  Transportation Research Part C, 84:142–157, September 2017.
  Transportation Research Part C.
  Audio slides.
  Contains material that was previously published in an AAMAS-17 paper.
  Details     BibTeX     Download: [pdf] (2.8MB )  [ps] (4.2MB )  

• A Protocol for Mixed Autonomous and Human-Operated Vehicles at Intersections.
  Guni Sharon and Peter Stone.
  In Gita Sukthankar and Juan A. Rodriguez-Aguilar, editors, Autonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Best Papers, Lecture Notes in Artificial Intelligence, pp. 151–67, Springer International Publishing, 2017.
  Details     BibTeX     Download: [pdf] (1.1MB )  [ps] (7.1MB )  [slides.pptx] (140.9MB )  

• Autonomous Intersection Management for Semi-Autonomous Vehicles.
  Tsz-Chiu Au, Shun Zhang, and Peter Stone.
  In Dusan Teodorovi'c, editors, Handbook of Transportation, pp. 88–104, Routledge, 2016.
  Details     BibTeX     Download: [pdf] (2.6MB )  

• Minimum Cost Matching for Autonomous Carsharing.
  Josiah P. Hanna, Michael Albert, Donna Chen, and Peter Stone.
  In Proceedings of the 9th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2016), June 2016.
  Details     BibTeX     Download: [pdf] (117.5kB )  [ps] (355.2kB )  [slides.pdf] (4.7MB )  

• Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas --- Final Report.
  Kara Kockelman, Stephen Boyles, Paul Avery, Christian Claudel, Lisa Loftus-Otway, Daniel Fagnant, Prateek Bansal, Michael Levin, Yong Zhao, Jun Liu, Lewis Clements, Wendy Wagner, Duncan Stewart, Guni Sharon, Michael Albert, Peter Stone, Josiah Hanna, Rahul Patel, Hagen Fritz, Tejas Choudhary, Tianxin Li, Aqshems Nichols, Kapil Sharma, and Michele Simoni.
  Technical Report 0-6838-2, The University of Texas at Austin Center for Transportation Research, 2016.
  Available online
  Details     BibTeX     Download: (unavailable)

• Auction-based autonomous intersection management.
  Dustin Carlino, Stephen D. Boyles, and Peter Stone.
  In Proceedings of the 16th IEEE Intelligent Transportation Systems Conference (ITSC), October 2013.
  Details     BibTeX     Download: [pdf] (318.1kB )  [ps] (1.4MB )  

• Evasion Planning for Autonomous Vehicles at Intersections.
  Tsz-Chiu Au, Chien-Liang Fok, Sriram Vishwanath, Christine Julien, and Peter Stone.
  In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2012.
  Details     BibTeX     Download: [pdf] (1.3MB )  [ps] (18.7MB )  

• Setpoint Scheduling for Autonomous Vehicle Controllers.
  Tsz-Chiu Au, Michael Quinlan, and Peter Stone.
  In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), May 2012.
  Details     BibTeX     Download: [pdf] (684.9kB )  [ps] (2.7MB )  

• Approximately Orchestrated Routing and Transportation Analyzer: Large-scale Traffic Simulation for Autonomous Vehicles.
  Dustin Carlino, Mike Depinet, Piyush Khandelwal, and Peter Stone.
  In Proceedings of the 15th IEEE Intelligent Transportation Systems Conference (ITSC), September 2012.
  Details     BibTeX     Download: [pdf] (727.1kB )  [ps] (14.3MB )  

• Automated Intersection Control: Performance of a Future Innovation Versus Current Traffic Signal Control.
  David Fajardo, Tsz-Chiu Au, Travis Waller, Peter Stone, and David Yang.
  Transportation Research Record (TRR), 2259:223–32, 2012.
  Details     BibTeX     Download: [pdf] (591.8kB )  

• A Platform for Evaluating Autonomous Intersection Management Policies.
  Chien-Liang Fok, Maykel Hanna, Seth Gee, Tsz-Chiu Au, Peter Stone, Christine Julien, and Sriram Vishwanath.
  In Proceedings of the ACM/IEEE Third International Conference on Cyber-Physical Systems (ICCPS), April 2012.
  Details     BibTeX     Download: [pdf] (5.7MB )  [ps] (61.7MB )  

• Enforcing Liveness in Autonomous Traffic Management.
  Tsz-Chiu Au, Neda Shahidi, and Peter Stone.
  In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence, August 2011.
  Details     BibTeX     Download: [pdf] (1.1MB )  [ps] (31.7MB )  

• Dynamic Lane Reversal in Traffic Management.
  Matthew Hausknecht, Tsz-Chiu Au, Peter Stone, David Fajardo, and Travis Waller.
  In Proceedings of IEEE Intelligent Transportation Systems Conference (ITSC), 2011.
  Details     BibTeX     Download: [pdf] (127.0kB )  [ps] (4.3MB )  

• Autonomous Intersection Management: Multi-Intersection Optimization.
  Matthew Hausknecht, Tsz-Chiu Au, and Peter Stone.
  In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2011.
  Details     BibTeX     Download: [pdf] (252.7kB )  [ps] (1.9MB )  

• Motion Planning Algorithms for Autonomous Intersection Management.
  Tsz-Chiu Au and Peter Stone.
  In AAAI 2010 Workshop on Bridging The Gap Between Task And Motion Planning (BTAMP), 2010.
  Details     BibTeX     Download: [pdf] (417.4kB )  [ps] (2.0MB )  

• Multi-Agent Social Simulation.
  Itsuki Noda, Peter Stone, Tomohisa Yamashita, and Koichi Kurumatani.
  In Nakashima, H., Aghajan, H., \& Augusto, J. C., editors, Handbook of Ambient Intelligence and Smart Environments, pp. 703–729, Springer Verlag, 2010.
  Official version from publisher's webage
  Details     BibTeX     Download: (unavailable)

• Bringing Simulation to Life: A Mixed Reality Autonomous Intersection.
  Michael Quinlan, Tsz-Chiu Au, Jesse Zhu, Nicolae Stiurca, and Peter Stone.
  In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010.
  Video available at http://www.cs.utexas.edu/~aim/video/MixedReality.wmv
  Details     BibTeX     Download: [pdf] (1.3MB )  [ps] (10.6MB )  

• A Multiagent Approach to Autonomous Intersection Management.
  Kurt Dresner and Peter Stone.
  Journal of Artificial Intelligence Research, 31:591–656, March 2008.
  Available from journal's web page.
  Further details and videos are on the project page.
  Details     BibTeX     Download: [pdf] (1.3MB )  [ps] (2.2MB )  

• Mitigating Catastrophic Failure at Intersections of Autonomous Vehicles.
  Kurt Dresner and Peter Stone.
  In AAMAS Workshop on Agents in Traffic and Transportation, pp. 78–85, Estoril, Portugal, May 2008.
  Details     BibTeX     Download: [pdf] (836.3kB )  [ps] (1.3MB )  

• Replacing the Stop Sign: Unmanaged Intersection Control for Autonomous Vehicles.
  Mark VanMiddlesworth, Kurt Dresner, and Peter Stone.
  In AAMAS Workshop on Agents in Traffic and Transportation, pp. 94–101, Estoril, Portugal, May 2008.
  Details     BibTeX     Download: [pdf] (161.4kB )  [ps] (305.1kB )  

• Sharing the Road: Autonomous Vehicles meet Human Drivers.
  Kurt Dresner and Peter Stone.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 1263–68, January 2007.
  IJCAI-07
  Details     BibTeX     Download: [pdf] (189.9kB )  [ps] (287.7kB )  

• Learning Policy Selection for Autonomous Intersection Management.
  Kurt Dresner and Peter Stone.
  In AAMAS 2007 Workshop on Adaptive and Learning Agents, pp. 34–39, Honolulu, Hawaii, USA, May 2007.
  Details     BibTeX     Download: [pdf] (79.9kB )  [ps] (197.6kB )  

• Multiagent Traffic Management: Opportunities for Multiagent Learning.
  Kurt Dresner and Peter Stone.
  In K. Tuyls et al., editors, LAMAS 2005, Lecture Notes in Artificial Intelligence, pp. 129–138, Springer Verlag, Berlin, 2006.
  LAMAS-05.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (81.4kB )  [ps] (168.2kB )  

• Human-Usable and Emergency Vehicle-Aware Control Policies for Autonomous Intersection Management.
  Kurt Dresner and Peter Stone.
  In AAMAS 2006 Workshop on Agents in Traffic and Transportation, May 2006.
  ATT 2006.
  The project page with videos from the paper.
  Details     BibTeX     Download: [pdf] (227.5kB )  [ps] (376.8kB )  

• Multiagent Traffic Management: An Improved Intersection Control Mechanism.
  Kurt Dresner and Peter Stone.
  In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, ACM Press, New York, NY, July 2005.
  Some videos referenced in the paper. The main project page
  Extended version citable as University of Texas at Austin AI lab technical report number UT-AI-TR-04-315
  AAMAS-2005
  Details     BibTeX     Download: [pdf] (143.5kB )  [ps] (216.7kB )  

• Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism.
  Kurt Dresner and Peter Stone.
  In The Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 530–537, July 2004.
  Some simulations of cars driving through intersections referenced in the paper. The main project page
  AAMAS-2004
  Details     BibTeX     Download: [pdf] (233.8kB )  [ps] (492.0kB )  

Computational Epidemiology

• Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies.
  Roberto Capobianco, Varun Kompella, James Ault, Guni Sharon, Stacy Jong, Spencer Fox, Lauren Meyers, Peter R. Wurman, and Peter Stone.
  The Journal of Artificial Intelligence Research (JAIR), 71:953–92, August 2021.
  Contains material that was previously published in an AAMAS 2021 paper and a AAAI 2020 Fall Symposium paper.
  Article available from JAIR website.
  Simulator source code.
  Details     BibTeX     Download: [pdf] (1.6MB )  

Game Theory

• VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pokémon.
  Cameron Angliss, Jiaxun Cui, Jiaheng Hu, Arrasy Rahman, and Peter Stone.
  In International Conference on Autonomous Agents and Multiagent Systems, May 2026.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents.
  Arrasy Rahman, Jiaxun Cui, and Peter Stone.
  In AAAI, February 2024.
  the conference presentation
  Details     BibTeX     Download: [pdf] (1.3MB )  [slides.ppt] (9.2MB )  [poster.pdf] (956.2kB )  

• MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection.
  Jiaxun Cui, Xiaomeng Yang, Mulong Luo, Geunbae Lee, Peter Stone, Hsien-Hsin S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong, and Yuandong Tian.
  In The Eleventh International Conference on Learning Representations (ICLR), May 2023.
  Video presentation
  Details     BibTeX     Download: [pdf] (769.8kB )  [slides.pdf] (2.1MB )  [poster.pdf] (1.8MB )  

• Program Embeddings for Rapid Mechanism Evaluation.
  Sai Kiran Narayanaswami, David Fridovich-Keil, Swarat Chaudhuri, and Peter Stone.
  In ICRA Workshop on Multi-Robot Learning, May 2023.
  Details     BibTeX     Download: [pdf] (1.3MB )  [poster.pdf] (916.2kB )  

• Sequential Online Chore Division for Autonomous Vehicle Convoy Formation.
  Harel Yedidsion, Shani Alkoby, and Peter Stone.
  Technical Report arXiv e-Prints 2104.04159, arXiv, 2021.
  arXiv
  Details     BibTeX     Download: [pdf] (478.9kB )  

• Marginal Cost Pricing with a Fixed Error Factor in Traffic Networks.
  Guni Sharon, Stephen D. Boyles, Shani Alkoby, and Peter Stone.
  In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19), May 2019.
  Details     BibTeX     Download: [pdf] (1.7MB )  [slides.pptx] (6.5MB )  

• DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation.
  Haipeng Chen, Bo An, Guni Sharon, Josiah P. Hanna, Peter Stone, Chunyan Miao, and Yeng Chai Soh.
  In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), February 2018.
  Details     BibTeX     Download: [pdf] (2.4MB )  [ps] (5.9MB )  

• Marginal Cost Pricing for System Optimal Traffic Assignment with Recourse under Supply-Side Uncertainty.
  Tarun Rambha, Stephen D. Boyles, Avinash Unnikrishnan, and Peter Stone.
  Transportation Research Part B: Methodological, 110:104–21, 2018.
  Official version from Publisher's Webpage
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Traffic Optimization For a Mixture of Self-interested and Compliant Agents.
  Guni Sharon, Michael Albert, Tarun Rambha, Stephen Boyles, and Peter Stone.
  In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), February 2018.
  Details     BibTeX     Download: [pdf] (1002.7kB )  [ps] (5.2MB )  [slides.pptx] (11.1MB )  

• Mechanism Design with Unknown Correlated Distributions: Can We Learn Optimal Mechanisms?.
  Michael Albert, Vincent Conitzer, and Peter Stone.
  In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS-17), May 2017.
  Details     BibTeX     Download: [pdf] (348.6kB )  [slides.pdf] (2.8MB )  

• Automated Design of Robust Mechanisms.
  Michael Albert, Vincent Conitzer, and Peter Stone.
  In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), Feb 2017.
  Details     BibTeX     Download: [pdf] (366.4kB )  [slides.pdf] (2.7MB )  

• Network-wide Adaptive Tolling for Connected and Automated vehicles.
  Guni Sharon, Michael W. Levin, Josiah P. Hanna, Tarun Rambha, Stephen D. Boyles, and Peter Stone.
  Transportation Research Part C, 84:142–157, September 2017.
  Transportation Research Part C.
  Audio slides.
  Contains material that was previously published in an AAMAS-17 paper.
  Details     BibTeX     Download: [pdf] (2.8MB )  [ps] (4.2MB )  

• Modeling Uncertainty in Leading Ad Hoc Teams.
  Noa Agmon, Samuel Barrett, and Peter Stone.
  In Proc. of 13th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), May 2014.
  Details     BibTeX     Download: [pdf] (255.6kB )  [ps] (1.7MB )  

• Leading a Best-Response Teammate in an Ad Hoc Team.
  Peter Stone, Gal A. Kaminka, and Jeffrey S. Rosenschein.
  In Esther David, Enrico Gerding, David Sarne, and Onn Shehory, editors, Agent-Mediated Electronic Commerce: Designing Trading Strategies and Mechanisms for Electronic Markets, pp. 132–146, Springer Verlag, November 2010.
  Official version from publisher's webpage
  Details     BibTeX     Download: [pdf] (179.3kB )  [ps] (226.4kB )  

• Online Multiagent Learning against Memory Bounded Adversaries.
  Doran Chakraborty and Peter Stone.
  In Machine Learning and Knowledge Discovery in Databases, pp. 211–26, September 2008.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (232.4kB )  [ps] (669.2kB )  

• A Polynomial-time Nash Equilibrium Algorithm for Repeated Games.
  Michael L. Littman and Peter Stone.
  Decision Support Systems, 39:55–66 , 2005.
  An earlier version appeared in the proceedings of the fourth annual ACM Conference on Electronic Commerce
  Official version from Decision Support Systems
  Details     BibTeX     Download: [pdf] (244.7kB )  [ps] (272.4kB )  

• Implicit Negotiation in Repeated Games.
  Michael L. Littman and Peter Stone.
  In Proceedings of The Eighth International Workshop on Agent Theories, Architectures, and Languages (ATAL-2001), pp. 393–404, August 2001.
  ATAL-2001
  Details     BibTeX     Download: [pdf] (181.2kB )  [ps] (149.4kB )  

General Game Playing

• Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia.
  Chandler Smith, Marwa Abdulhai, Manfred Diaz, Marko Tesic, Rakshit S. Trivedi, Alexander Sasha Vezhnevets, Lewis Hammond, Jesse Clifton, Minsuk Chang, Edgar A. Duenez-Guzmán, John P. Agapiou, Jayd Matyas, Danny Karmon, Akash Kundu, Aliaksei Korshuk, Ananya Ananya, Arrasy Rahman, Avinaash Anand Kulandaivel, Bain McHale, Beining Zhang, Buyantuev Alexander, Carlos Saith Rodriguez Rojas, Caroline Wang, Chetan Talele, Chenao Liu, Chichen Lin, Diana Riazi, Di Yang Shi, Emanuel Tewolde, Elizaveta Tennant, Fangwei Zhong, Fuyang Cui, Gang Zhao, Gema Parreno Piqueras, Hyeonggeun Yun, Ilya Makarov, Jiaxun Cui, Jebish Purbey, Jim Dilkes, Jord Nguyen, Lingyun Xiao, Luis Felipe Giraldo, Manuela Chacon-Chamorro, Manuel Sebastian Rios Beltran, Marta Emili Garcia Segura, Mengmeng Wang, Mogtaba Alim, Nicanor Quijano, Nico Schiavone, Olivia Macmillan-Scott, Oswaldo Pena, Peter Stone, Ram Mohan Rao Kadiyala, Rolando Fernandez, Ruben Manrique, Sunjia Lu, Sheila A. McIlraith, Shamika Dhuri, Shuqing Shi, Siddhant Gupta, Sneheel Sarangi, Sriram Ganapathi Subramanian, Taehun Cha, Toryn Q. Klassen, Wenming Tu, Weijian Fan, Wu Ruiyang, Xue Feng, Yali Du, Yang Liu, Yiding Wang, Yipeng Kang, Yoonchang Sung, Yuxuan Chen, Zhaowei Zhang, Zhihan Wang, Zhiqiang Wu, Ziang Chen, Zilong Zheng, Zixia Jia, Ziyan Wang, Dylan Hadfield-Menell, Natasha Jaques, Tim Baarslag, Jose Hernandez-Orallo, and Joel Z. Leibo.
  In Conference on Neural Information Processing Systems (NeurIPS, September 2025.
  Details     BibTeX     Download: [pdf] (4.1MB )  

• The Impact of Determinism on Learning Atari 2600 Games.
  Matthew Hausknecht and Peter Stone.
  In AAAI Workshop on Learning for General Competency in Video Games, January 2015.
  Details     BibTeX     Download: [pdf] (65.1kB )  

• Deep Recurrent Q-Learning for Partially Observable MDPs.
  Matthew Hausknecht and Peter Stone.
  In AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (AAAI-SDMIA15), November 2015.
  Details     BibTeX     Download: [pdf] (1.5MB )  [slides.pdf] (3.8MB )  

• A Neuroevolution Approach to General Atari Game Playing.
  Matthew Hausknecht, Joel Lehman, Risto Miikkulainen, and Peter Stone.
  IEEE Transactions on Computational Intelligence and AI in Games, 2014.
  Details     BibTeX     Download: [pdf] (1.3MB )  [ps] (3.1MB )  

• General Game Learning using Knowledge Transfer.
  Bikramjit Banerjee and Peter Stone.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 672–677, January 2007.
  IJCAI-07
  Details     BibTeX     Download: [pdf] (118.1kB )  [ps] (198.5kB )  

• Graph-Based Domain Mapping for Transfer Learning in General Games.
  Gregory Kuhlmann and Peter Stone.
  In Proceedings of The Eighteenth European Conference on Machine Learning, September 2007.
  Details     BibTeX     Download: [pdf] (377.0kB )  [ps] (601.6kB )  

• Value Function Transfer for General Game Playing.
  Bikramjit Banerjee, Gregory Kuhlmann, and Peter Stone.
  In ICML workshop on Structural Knowledge Transfer for Machine Learning, June 2006.
  ICML 2006 workshop on Structural Knowledge Transfer for Machine Learning
  Details     BibTeX     Download: [pdf] (172.9kB )  [ps] (464.0kB )  

• Automatic Heuristic Construction in a Complete General Game Player.
  Gregory Kuhlmann, Kurt Dresner, and Peter Stone.
  In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1457–62, July 2006.
  AAAI 2006
  Details     BibTeX     Download: [pdf] (124.1kB )  [ps] (156.2kB )  

Machine Learning

• Hakken: Predicting future discoveries to fill the gaps in today's knowledge.
  Tarek R. Besold, Uchenna Akujuobi, Pablo Sanchez, Alessandra Toniato, Kana Maruyama, Jihun Choi, Samy Badreddine, Frederick Gifford, Daniel Evans-Yamamoto, Sucheendra K. Palaniappan, Miquel Ferrer, Kae Nagano, Iris Rossell, Tom Joy, Hatem ElShazly, Chrysa Iliopoulou, Christoph Wehner, Thiviyan Thanapalasingam, Susana Nunes, Pedro G. Cotovio, Peter Wurman, Peter Stone, Hiroaki Kitano, and Michael Spranger.
  Technical Report arXiv e-Prints 2609.04494, arXiv, 2026.
  arXiv version
  Details     BibTeX     Download: (unavailable)

• Coachable agents for interactive gameplay.
  Roberto Capobianco, Harm van Seijen, Nolan D. Bard, Neil Burch, Fatima Davelouis, Josh Davidson, Alisa Devlic, Yunshu Du, Ishan Durugkar, Siddhant Gangapurwala, Daniel Hernandez, G. Zacharias Holland, Sahil Jain, Kenta Kawamoto, Raksha Kumaraswamy, Patrick MacAlpine, Dustin R. Morrill, Declan Oller, Francesco Riccio, Akanksha Saran, Craig Sherstan, Kaushik Subramanian, Thomas J. Walsh, Samuel Barrett, Kizza N. Frisbee, Mady Govil, Johannes Günther, Varun R. Kompella, James A. MacGlashan, Maxwell Svetlik, Michael D. Thomure, Jaden B. Travnik, Kevin Waugh, Elahe Aghapour, Florian Fuchs, Andreanne Lemay, Shruti Mishra, Takuma Seno, Peter Stone, Michael Spranger, and Peter R. Wurman.
  Technical Report arXiv e-Prints 2607.00642, arXiv, 2026.
  arXiv version
  Details     BibTeX     Download: [pdf] (9.1MB )  

• LEACL: LLM-Enhanced Automatic Curriculum Learning for Reinforcement Learning in Long-Horizon Manipulation Tasks.
  Faraz Heravi, James Ouyang, Zifan Xu, Arjun Kumar, Yoonchang Sung, and Peter Stone.
  In The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2026.
  Details     BibTeX     Download: [pdf] (5.9MB )  

• Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning.
  Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, and Roberto Martin-Martin.
  Reinforcement Learning Journal, 7, August 2026.
  Official version
  Details     BibTeX     Download: [pdf] (7.9MB )  

• RoboSSM: Scalable In-Context Imitation Learning Via State-Space Models.
  Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Martín-Martín, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2026.
  Details     BibTeX     Download: [pdf] (2.8MB )  

• MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention.
  Yuxin Chen, Chen Tang, Jianglan Wei, Chenran Li, Ran Tian, Xiang Zhang, Wei Zhan, Peter Stone, and Masayoshi Tomizuka.
  In Conference on Robot Learning (CoRL), September 2025.
  Details     BibTeX     Download: [pdf] (18.9MB )  

• Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination.
  Saad Abdul Ghani, Zizhao Wang, Peter Stone, and and Xuesu Xiao.
  In International Conference on Intelligent Robots and Systems, October 2025.
  Details     BibTeX     Download: [pdf] (2.1MB )  

• ComposableNav: Instruction-Following Navigation in Dynamic Environments via Composable Diffusion.
  Zichao Hu, Chen Tang, Michael J. Munje, Yifeng Zhu, Alex Liu, Shuijing Liu, Garrett Warnell, Peter Stone, and Joydeep Biswas.
  In Conference on Robot Learning, September 2025.
  Details     BibTeX     Download: [pdf] (12.0MB )  

• SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation.
  Michael J. Munje, Chen Tang, Shuijing Liu, Zichao Hu, Yifeng Zhu, Jiaxun Cui, Garrett Warnell, Joydeep Biswas, and Peter Stone.
  In Conference on Robot Learning, September 2025.
  Details     BibTeX     Download: [pdf] (22.5MB )  

• PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation.
  Mingyo Seo, Yoonyoung Cho, Yoonchang Sung, Peter Stone, Yuke Zhu, and Beomjoon Kim.
  In IEEE International Conference on Robotics and Automation (ICRA), May 2025.
  Details     BibTeX     Download: [pdf] (1.4MB )  [slides.pdf] (2.1MB )  [poster.pdf] (1.3MB )  

• Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia.
  Chandler Smith, Marwa Abdulhai, Manfred Diaz, Marko Tesic, Rakshit S. Trivedi, Alexander Sasha Vezhnevets, Lewis Hammond, Jesse Clifton, Minsuk Chang, Edgar A. Duenez-Guzmán, John P. Agapiou, Jayd Matyas, Danny Karmon, Akash Kundu, Aliaksei Korshuk, Ananya Ananya, Arrasy Rahman, Avinaash Anand Kulandaivel, Bain McHale, Beining Zhang, Buyantuev Alexander, Carlos Saith Rodriguez Rojas, Caroline Wang, Chetan Talele, Chenao Liu, Chichen Lin, Diana Riazi, Di Yang Shi, Emanuel Tewolde, Elizaveta Tennant, Fangwei Zhong, Fuyang Cui, Gang Zhao, Gema Parreno Piqueras, Hyeonggeun Yun, Ilya Makarov, Jiaxun Cui, Jebish Purbey, Jim Dilkes, Jord Nguyen, Lingyun Xiao, Luis Felipe Giraldo, Manuela Chacon-Chamorro, Manuel Sebastian Rios Beltran, Marta Emili Garcia Segura, Mengmeng Wang, Mogtaba Alim, Nicanor Quijano, Nico Schiavone, Olivia Macmillan-Scott, Oswaldo Pena, Peter Stone, Ram Mohan Rao Kadiyala, Rolando Fernandez, Ruben Manrique, Sunjia Lu, Sheila A. McIlraith, Shamika Dhuri, Shuqing Shi, Siddhant Gupta, Sneheel Sarangi, Sriram Ganapathi Subramanian, Taehun Cha, Toryn Q. Klassen, Wenming Tu, Weijian Fan, Wu Ruiyang, Xue Feng, Yali Du, Yang Liu, Yiding Wang, Yipeng Kang, Yoonchang Sung, Yuxuan Chen, Zhaowei Zhang, Zhihan Wang, Zhiqiang Wu, Ziang Chen, Zilong Zheng, Zixia Jia, Ziyan Wang, Dylan Hadfield-Menell, Natasha Jaques, Tim Baarslag, Jose Hernandez-Orallo, and Joel Z. Leibo.
  In Conference on Neural Information Processing Systems (NeurIPS, September 2025.
  Details     BibTeX     Download: [pdf] (4.1MB )  

• Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference-Aligned Path Planning.
  Haresh, Karnan; Elvin, Yang; Garrett, Warnell; Joydeep, Biswas; Peter, and Stone.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (4.3MB )  

• Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents.
  Arrasy Rahman, Jiaxun Cui, and Peter Stone.
  In AAAI, February 2024.
  the conference presentation
  Details     BibTeX     Download: [pdf] (1.3MB )  [slides.ppt] (9.2MB )  [poster.pdf] (956.2kB )  

• Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds.
  Amir Hossain Raj, Zichao Hu, Haresh Karnan, Rohan Chandra, Amirreza Payandeh, Luisa Mao, Peter Stone, Joydeep Biswas, and and Xuesu Xiao.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning.
  Zizhao Wang, Caroline Wang, Xuesu Xiao, Yuke Zhu, and Peter Stone.
  In AAAI Conference on Artificial Intelligence, February 2024.
  Details     BibTeX     Download: [pdf] (4.5MB )  

• t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making.
  William Yue, Bo Liu, and Peter Stone.
  In Conference on Lifelong Learning Agents (CoLLAs), July 2024.
  Details     BibTeX     Download: [pdf] (599.7kB )  [poster.pdf] (709.1kB )  

• VaryNote: A Method to Automatically Vary the Number of Notes in Symbolic Music.
  Juan M. Huerta, Bo Liu, and Peter Stone.
  In The 16th International Symposium on Computer Music Multidisciplinary Research, (CMMR), Springer, November 2023.
  the conference presentation
  Details     BibTeX     Download: [pdf] (1.4MB )  [slides.pdf] (2.3MB )  

• Models of human preference for learning reward functions.
  W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, and Alessandro Allievi.
  Transactions on Machine Learning Research (TMLR), 2023.
  Details     BibTeX     Download: [pdf] (6.7MB )  [slides.pdf] (13.4MB )  

• FAMO: Fast Adaptive Multitask Optimization.
  Bo Liu, Yihao Feng, Peter Stone, and Qiang Liu.
  In Neural Information Processing Systems Foundation, July 2023.
  Details     BibTeX     Download: [pdf] (3.0MB )  

• LIBERO: Benchmarking Knowledge Transfer in Lifelong Robot Learning.
  Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone.
  In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks, December 2023.
  Details     BibTeX     Download: [pdf] (37.6MB )  [poster.pdf] (2.6MB )  

• Composing Efficient, Robust Tests for Policy Selection.
  Dustin Morrill, Thomas J. Walsh, Daniel Hernandez, Peter R. Wurman, and Peter Stone.
  In The 39th Conference on Uncertainty in Artificial Intelligence (UAI), August 2023.
  short video presentation, poster
  Details     BibTeX     Download: [pdf] (5.5MB )  

• Walking and Falling: Using Robot Simulations to Model the Role of Errors in Infant Walking.
  Ossmy, Ori, Han, Danyang, MacAlpine, Patrick, Hoch, Justine, Stone, Peter, and Adolph, Karen E..
  Developmental Science, 27:e13449, September 2023.
  Available from the publisher's webpage
  Details     BibTeX     Download: (unavailable)

• Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways.
  Jinsoo Park, Xuesu Xiao, Garrett Warnell, Harel Yedidsion, and Peter Stone.
  In Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), May 2023.
  6-minute video presentation
  Details     BibTeX     Download: [pdf] (4.6MB )  [slides.pptx] (21.3MB )  [poster.pdf] (1.1MB )  

• Multimodal Embodied Attribute Learning by Robots for Object-Centric Action Policies.
  Xiaohan Zhang, Saeid Amiri, Jivko Sinapov, Jesse Thomason, Peter Stone, and Shiqi Zhang.
  Autonomous Robots, March 2023.
  Official version on publisher's website
  Details     BibTeX     Download: [pdf] (2.6MB )  

• Socially CompliAnt Navigation Dataset (SCAND): A Large-Scale Dataset Of Demonstrations For Social Navigation.
  Haresh Karnan, Anirudh Nair, Xuesu Xiao, Garrett Warnell, Soren Pirk, Alexander Toshev, Justin Hart, Joydeep Biswas, and Peter Stone.
  Robotics and Automation Letters (RA-L), 2022, 7:11807–14, October 2022.
  Dataset; Poster; Video Presentation
  Details     BibTeX     Download: [pdf] (4.2MB )  

• Effective Mutation Rate Adaptation through Group Elite Selection.
  Akarsh Kumar, Bo Liu, Risto Miikkulainen, and Peter Stone.
  In Proceedings of the Genetic and Evolutionary Computation Conference, July 2022.
  Details     BibTeX     Download: [pdf] (9.7MB )  

• BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach.
  Bo Liu, Mao Ye, Stephen Wright, Peter Stone, and Qiang Liu.
  In Conference on Neural Information Processing Systems, 2022, December 2022.
  Details     BibTeX     Download: [pdf] (4.2MB )  [slides.pdf] (1.6MB )  [poster.pdf] (885.6kB )  

• Dynamic Sparse Training for Deep Reinforcement Learning.
  Ghada Sokar, Elena Mocanu, Decebal Constantin Mocanu, Mykola Pechenizkiy, and Peter Stone.
  In Proceedings of the 31st International Joint Conference on Artificial Intelligence, July 2022.
  arXiv version with the appendix
  Details     BibTeX     Download: [pdf] (3.7MB )  [slides.pptx] (16.7MB )  

• APPL: Adaptive Planner Parameter Learning.
  Xuesu Xiao, Zizhao Wang, Zifan Xu, Bo Liu, abd Gauraang Dhamankar, Anirudh Nair, Garrett Warnell, and Peter Stone.
  Robotics and Autonomous Systems, May 2022.
  Details     BibTeX     Download: [pdf] (2.6MB )  

• Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey.
  Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone.
  Autonomous Robots, 46:569–97, March 2022.
  Details     BibTeX     Download: [pdf] (1.3MB )  

• Machine versus Human Attention in Deep Reinforcement Learning Tasks.
  Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu, Mary Hayhoe, Dana Ballard, and Peter Stone.
  In Conference on Neural Information Processing Systems (NeurIPS), December 2021.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Efficient Real-Time Inference in Temporal Convolution Networks.
  Piyush Khandelwal, James MacGlashan, Peter Wurman, and Peter Stone.
  In Proceedings of the 2021 International Conference on Robotics and Automation (ICRA 2021), May 2021.
  Details     BibTeX     Download: [pdf] (346.4kB )  

• Conflict-Averse Gradient Descent for Multi-task learning.
  Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu.
  In Conference on Neural Information Processing Systems (NeurIPS), 2021, December 2021.
  slides and 9-minute presentation
  github repository
  Details     BibTeX     Download: [pdf] (9.7MB )  

• Is the Cerebellum a Model-Based Reinforcement Learning Agent?.
  Bharath Masetty, Reuth Mirsky, Ashish D. Deshpande, Michael Mauk, and Peter Stone.
  In Adaptive and Learning Agents Workshop at AAMAS, May 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (594.9kB )  [slides.pdf] (1.5MB )  

• Learning and Reasoning for Robot Dialog and Navigation Tasks.
  Keting Lu, Shiqi Zhang, Peter Stone, and Xiaoping Chen.
  In Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 107–117, Association for Computational Linguistics, 1st virtual meeting, July 2020.
  Official version from ACL Digital Library, including a link to the conference presentation
  Details     BibTeX     Download: [pdf] (3.6MB )  

• Reducing Sampling Error in Batch Temporal Difference Learning.
  Brahma Pavse, Ishan Durugkar, Josiah Hanna, and Peter Stone.
  In Proceedings of the 37th International Conference on Machine Learning (ICML), July 2020.
  The paper and talk is available from the ICML 2020 virtual conference page.
  Details     BibTeX     Download: [pdf] (738.4kB )  [slides.pdf] (5.2MB )  

• On Sampling Error in Batch Action-Value Prediction Algorithms.
  Brahma S. Pavse, Josiah P. Hanna, Ishan Durugkar, and Peter Stone.
  In In the Offline Reinforcement Learning Workshop at Neural Information Processing Systems (NeurIPS), December 2020., December 2020.
  5-minute Video Presentation
  Details     BibTeX     Download: [pdf] (327.2kB )  

• Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks.
  Lemeng Wu, Bo Liu, Peter Stone, and Qiang Liu.
  In Advances in Neural Information Processing Systems 34 (2020), December 2020.
  Details     BibTeX     Download: [pdf] (8.1MB )  [slides.pdf] (744.8kB )  

• Building Self-Play Curricula Online by Playing with Expert Agents in Adversarial Games.
  Felipe Leno Da Silva, Anna Helena Reali Costa, and Peter Stone.
  In Proceedings of the 8th Brazilian Conference on Intelligent Systems (BRACIS), October 2019.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Multi-modal Predicate Identification using Dynamically Learned Robot Controllers.
  Saeid Amiri, Suhua Wei, Shiqi Zhang, Jivko Sinapov, Jesse Thomason, and Peter Stone.
  In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), July 2018.
  Details     BibTeX     Download: [pdf] (2.5MB )  

• On the Impact of Music on Decision Making in Cooperative Tasks.
  Elad Liebman, Corey N. White, and Peter Stone.
  In 19th International Society for Music Information retrieval Conference (ISMIR), September 2018.
  Details     BibTeX     Download: [pdf] (258.5kB )  

• Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity Inspired by Network Science.
  Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H. Nguyen, Madeleine Gibescu, and Antonio Liotta.
  Nature Communications, 9(2383), June 2018.
  Official version from Publisher's Webpage.
  Details     BibTeX     Download: [pdf] (1.5MB )  

• Variety Wins: Soccer-Playing Robots and Infant Walking.
  Ori Ossmy, Justine E. Hoch, Patrick MacAlpine, Shohan Hasan, Peter Stone, and Karen E. Adolph.
  Frontiers in Neurorobotics, 12:19, 2018.
  Available from the publisher's webpage
  Details     BibTeX     Download: [pdf] (2.9MB )  

• Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions.
  Jesse Thomason, Jivko Sinapov, Raymond J. Mooney, and Peter Stone.
  In Proceedings of the 32nd Conference on Artificial Intelligence (AAAI), February 2018.
  Details     BibTeX     Download: [pdf] (1.4MB )  

• Multiagent Learning Paradigms.
  Karl Tuyls and Peter Stone.
  In Francesco Belardinelli and Estefania Argente, editors, Multi-Agent Systems and Agreement Technologies, Lecture Notes in Artificial Intelligence, pp. 3–21, Springer, 2018.
  Details     BibTeX     Download: [pdf] (509.0kB )  

• Machine Learning Capabilities of a Simulated Cerebellum.
  Matthew Hausknecht, Wen-Ke Li, Michael Mauk, and Peter Stone.
  "IEEE Transactions on Neural Networks and Learning Systems", 28(3):510–22, March 2017.
  Details     BibTeX     Download: [pdf] (1.1MB )  

• UT Austin Villa RoboCup 3D Simulation Base Code Release.
  Patrick MacAlpine and Peter Stone.
  In Sven Behnke, Daniel D. Lee, Sanem Sariel, and Raymond Sheh, editors, RoboCup 2016: Robot Soccer World Cup XX, Lecture Notes in Artificial Intelligence, pp. 135–43, Springer Verlag, Berlin, 2017.
  Code release at https://github.com/LARG/utaustinvilla3d
  Details     BibTeX     Download: [pdf] (394.2kB )  [ps] (2.1MB )  [slides.pdf] (107.1MB )  

• Bin-Based Estimation of the Amount of Effort for Embedded Software Development Projects with Support Vector Machines.
  Kazunori Iwata, Elad Liebman, Peter Stone, Toyoshiro Nakashima, Yoshiyuki Anan, and Naohiro Ishii.
  In Roger Lee, editors, Computer and Information Science 2015, Studies in Computational Intelligence, Springer Verlag, Berlin, 2016.
  Details     BibTeX     Download: [pdf] (807.5kB )  

• Impact of Music on Decision Making in Quantitative Tasks.
  Elad Liebman, Peter Stone, and Corey N. White.
  In 17th International Society for Music Information retrieval Conference (ISMIR), August 2016.
  Details     BibTeX     Download: [pdf] (591.1kB )  [slides.pdf] (678.6kB )  

• Adaptation of Surrogate Tasks for Bipedal Walk Optimization.
  Patrick MacAlpine, Elad Liebman, and Peter Stone.
  In GECCO Surrogate-Assisted Evolutionary Optimisation (SAEOpt) Workshop, July 2016.
  Details     BibTeX     Download: [pdf] (172.1kB )  [ps] (739.5kB )  [slides.pdf] (165.0MB )  

• When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing.
  Fei Fang, Peter Stone, and Milind Tambe.
  In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), July 2015.
  Winner of Computational Sustainability Track Outstanding Paper Award at IJCAI 2015
  Details     BibTeX     Download: [pdf] (456.7kB )  [ps] (983.9kB )  [slides.pptx] (6.2MB )  

• Representative Selection in Nonmetric Datasets.
  Elad Liebman, Benny Chor, and Peter Stone.
  "Applied Artificial Intelligence", 29:807–838, 2015.
  Details     BibTeX     Download: [pdf] (846.3kB )  

• How Music Alters Decision Making: Impact of Music Stimuli on Emotional Classification.
  Elad Liebman, Peter Stone, and Corey N. White.
  In 16th International Society for Music Information retrieval Conference (ISMIR), October 2015.
  Details     BibTeX     Download: [pdf] (832.6kB )  [ps] (6.3MB )  [slides.pdf] (2.0MB )  

• TacTex'13: A Champion Adaptive Power Trading Agent.
  Daniel Urieli and Peter Stone.
  In Proceedings of the Twenty-Eighth Conference on Artificial Intelligence (AAAI 2014), July 2014.
  Details     BibTeX     Download: [pdf] (257.8kB )  [ps] (7.3MB )  [slides.pdf] (3.8MB )  

• HyperNEAT-GGP: A HyperNEAT-based Atari General Game Player.
  Matthew Hausknecht, Piyush Khandelwal, Risto Miikkulainen, and Peter Stone.
  In Genetic and Evolutionary Computation Conference (GECCO), July 2012.
  Details     BibTeX     Download: [pdf] (880.9kB )  [ps] (3.3MB )  

• PAC Subset Selection in Stochastic Multi-armed Bandits.
  Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, and Peter Stone.
  In Proceedings of the 29th International Conference on Machine Learning (ICML), pp. 655–662, Omnipress, New York, NY, USA, June-July 2012.
  Details     BibTeX     Download: [pdf] (230.2kB )  [ps] (519.5kB )  

• Using a million cell simulation of the cerebellum: Network scaling and task generality.
  Wen-Ke Li, Matthew J. Hausknecht, Peter Stone, and Michael D. Mauk.
  Neural Networks, November 2012.
  Details     BibTeX     Download: [pdf] (765.8kB )  [ps] (2.5MB )  

• Learning Powerful Kicks on the Aibo ERS-7: The Quest for a Striker.
  Matthew Hausknecht and Peter Stone.
  In Javier Ruiz-del-Solar, Eric Chown, and Paul G. Plöger, editors, RoboCup-2010: Robot Soccer World Cup XIV, Lecture Notes in Artificial Intelligence, pp. 254–65, Springer Verlag, Berlin, 2011.
  Video and source code available at http://www.cs.utexas.edu/~AustinVilla/?p=research/aibo_kick
  Details     BibTeX     Download: [pdf] (324.4kB )  [ps] (2.8MB )  

• Empowerment for continuous agent-environment systems.
  Tobias Jung, Daniel Polani, and Peter Stone.
  Adaptive Behavior, 19(1):16–39, 2011.
  Available from Adaptive Behavior page.
  Details     BibTeX     Download: [pdf] (1.8MB )  [ps] (1.8MB )  

• Efficient Selection of Multiple Bandit Arms: Theory and Practice.
  Shivaram Kalyanakrishnan and Peter Stone.
  In Proceedings of the Twenty-seventh International Conference on Machine Learning (ICML), 2010.
  Details     BibTeX     Download: [pdf] (257.1kB )  [ps] (679.3kB )  

• Autonomous Return on Investment Analysis of Additional Processing Resources.
  Jonathan Wildstrom, Peter Stone, and Emmett Witchel.
  International Journal on Autonomic Computing, 1(3):280–296, Inderscience Publishers, Inderscience Publishers, Geneva, SWITZERLAND, 2010.
  IJAC
  Details     BibTeX     Download: (unavailable)

• Compositional Models for Reinforcement Learning.
  Nicholas K. Jong and Peter Stone.
  In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2009.
  Details     BibTeX     Download: [pdf] (173.0kB )  [ps] (431.7kB )  

• Adapting Price Predictions in TAC SCM.
  David Pardoe and Peter Stone.
  In John Collins, Peyman Faratin, Simon Parsons, Juan A. Rodriguez-Aguilar, Norman M. Sadeh, Onn Shehory, and Elizabeth Sklar, editors, Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, Lecture Notes in Business Information Processing, pp. 30–45, Springer Verlag, 2009.
  Official version from publisher's webpage
  Details     BibTeX     Download: [pdf] (328.7kB )  [ps] (524.0kB )  

• Online Multiagent Learning against Memory Bounded Adversaries.
  Doran Chakraborty and Peter Stone.
  In Machine Learning and Knowledge Discovery in Databases, pp. 211–26, September 2008.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (232.4kB )  [ps] (669.2kB )  

• A Neural Network-Based Approach to Robot Motion Control.
  Uli Grasemann, Daniel Stronger, and Peter Stone.
  In Ubbo Visser, Fernando Ribeiro, Takeshi Ohashi, and Frank Dellaert, editors, RoboCup-2007: Robot Soccer World Cup XI, Lecture Notes in Artificial Intelligence, pp. 480–87, Springer Verlag, Berlin, 2008.
  Details     BibTeX     Download: [pdf] (280.9kB )  [ps] (1.6MB )  

• The Utility of Temporal Abstraction in Reinforcement Learning.
  Nicholas K. Jong, Todd Hester, and Peter Stone.
  In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.
  AAMAS-2008
  Details     BibTeX     Download: [pdf] (136.4kB )  [ps] (325.7kB )  

• Online Kernel Selection for Bayesian Reinforcement Learning.
  Joseph Reisinger, Peter Stone, and Risto Miikkulainen.
  In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008.
  ICML 2008
  Details     BibTeX     Download: [pdf] (434.0kB )  [ps] (1.7MB )  

• Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents.
  Daniel Stronger and Peter Stone.
  International Journal on Artificial Intelligence Tools, 17(1):159–174, February 2008.
  official published version
  Details     BibTeX     Download: [pdf] (236.4kB )  [ps] (1.0MB )  

• Structure Based Color Learning on a Mobile Robot under Changing Illumination.
  Mohan Sridharan and Peter Stone.
  Autonomous Robots, 23(3):161–182, 2007.
  Official versionfrom the Autonomous Robots publisher's webpage.
  Details     BibTeX     Download: [pdf] (2.0MB )  [ps] (8.6MB )  

• Planning Actions to Enable Color Learning on a Mobile Robot.
  Mohan Sridharan and Peter Stone.
  International Journal of Information and Systems Sciences, 3(3):510–25, 2007.
  official published version
  Details     BibTeX     Download: (unavailable)

• Action Selection for Illumination Invariant Color Learning.
  Mohan Sridharan and Peter Stone.
  In The IEEE International Conference on Intelligent Robots and Systems (IROS), 2007.
  Details     BibTeX     Download: [pdf] (176.5kB )  [ps] (490.5kB )  

• Color Learning on a Mobile Robot: Towards Full Autonomy under Changing Illumination.
  Mohan Sridharan and Peter Stone.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 2212–2217, January 2007.
  IJCAI-07
  Details     BibTeX     Download: [pdf] (129.5kB )  [ps] (184.9kB )  

• Multiagent learning is not the answer. It is the question.
  Peter Stone.
  Artificial Intelligence, 171:402–05, May 2007.
  Response to Shoham, Powers, and Grenager If Multi-Agent Systems is the Answer, What is the Question?, available from Shoham's webpage.
  Official version from the AIJ publisher's webpage.
  Details     BibTeX     Download: [pdf] (117.9kB )  [ps] (91.0kB )  

• Learning and Multiagent Reasoning for Autonomous Agents.
  Peter Stone.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 13–30, January 2007.
  IJCAI-07
  Details     BibTeX     Download: [pdf] (308.5kB )  [ps] (379.3kB )  

• Machine Learning for On-Line Hardware Reconfiguration.
  Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 1113–1118, January 2007.
  IJCAI-07
  Details     BibTeX     Download: [pdf] (98.6kB )  [ps] (191.7kB )  

• Multiagent Traffic Management: Opportunities for Multiagent Learning.
  Kurt Dresner and Peter Stone.
  In K. Tuyls et al., editors, LAMAS 2005, Lecture Notes in Artificial Intelligence, pp. 129–138, Springer Verlag, Berlin, 2006.
  LAMAS-05.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (81.4kB )  [ps] (168.2kB )  

• Autonomous Planned Color Learning on a Mobile Robot Without Labeled Data.
  Mohan Sridharan and Peter Stone.
  In The Ninth International Conference on Control, Automation, Robotics and Vision, December 2006.
  ICARCV 2006
  Details     BibTeX     Download: [pdf] (444.1kB )  [ps] (2.3MB )  

• Adapting to Workload Changes Through On-The-Fly Reconfiguration.
  Jonathan Wildstrom, Peter Stone, Emmett Witchel, and Mike Dahlin.
  Technical Report UT-AI-TR-06-330, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2006.
  At ftp://ftp.cs.utexas.edu/pub/AI-Lab/tech-reports/UT-AI-TR-06-330.pdf
  Details     BibTeX     Download: (unavailable)

• Towards Illumination Invariance in the Legged League.
  Mohan Sridharan and Peter Stone.
  In Daniele Nardi, Martin Riedmiller, and Claude Sammut, editors, RoboCup-2004: Robot Soccer World Cup VIII, Lecture Notes in Artificial Intelligence, pp. 196–208, Springer Verlag, Berlin, 2005.
  Some videos of robots referenced in the paper.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (245.1kB )  [ps] (2.1MB )  

• Autonomous Color Learning on a Mobile Robot.
  Mohan Sridharan and Peter Stone.
  In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
  Some videos of the robot referenced in the paper.
  AAAI 2005
  Details     BibTeX     Download: [pdf] (901.5kB )  [ps] (6.4MB )  

• Real-Time Vision on a Mobile Robot Platform.
  Mohan Sridharan and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), August 2005.
  Some videos of the robot referenced in the paper.
  IROS-2005
  Details     BibTeX     Download: [pdf] (396.1kB )  [ps] (5.0MB )  

• Towards Self-Configuring Hardware for Distributed Computer Systems.
  Jonathan Wildstrom, Peter Stone, Emmett Witchel, Raymond J. Mooney, and Mike Dahlin.
  In The Second International Conference on Autonomic Computing, pp. 241–249, June 2005.
  ICAC-05
  A revised version of the paper appeared on IBM's Developer Works website
  Details     BibTeX     Download: [pdf] (74.0kB )  [ps] (104.6kB )  

• Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions.
  Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, and David McAllester.
  Journal of Artificial Intelligence Research, 19:209–242, 2003.
  Available from journal's web page.
  Details     BibTeX     Download: [pdf] [ps] 

• Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation.
  Robert E. Schapire, Peter Stone, David McAllester, Michael L. Littman, and János A. Csirik.
  In Proceedings of the Nineteenth International Conference on Machine Learning, 2002.
  ICML-2002
  Details     BibTeX     Download: [pdf] (159.5kB )  [ps] (137.2kB )  

• ATTac-2000: An Adaptive Autonomous Bidding Agent.
  Peter Stone, Michael L. Littman, Satinder Singh, and Michael Kearns.
  Journal of Artificial Intelligence Research, 15:189–206, June 2001.
  Available from journal's web page.
  Details     BibTeX     Download: [pdf] (326.9kB )  [ps] (305.2kB )  

• Multiagent Systems: A survey from a machine learning perspective.
  Peter Stone and Manuela Veloso.
  Autonomous Robots, 8(3):345–383, July 2000.
  Formerly citable as Carnegie Mellon University CS technical report number CMU-CS-97-193. December, 1997.
  Details     BibTeX     Download: [pdf] (292.1kB )  [ps] (1.1MB )  

• Using Decision Tree Confidence Factors for Multiagent Control.
  Peter Stone and Manuela Veloso.
  In Hiroaki Kitano, editors, RoboCup-97: Robot Soccer World Cup I, Lecture Notes in Artificial Intelligence, pp. 99–111, Springer Verlag, Berlin, 1998.
  HTML version.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (157.2kB )  [ps] (128.0kB )  

• Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function.
  Peter Stone and Manuela Veloso.
  In Advances in Neural Information Processing Systems 8, pp. 896–902, MIT Press, Cambridge, MA, 1996.
  NIPS-95
  HTML version.
  Details     BibTeX     Download: [pdf] (300.1kB )  [ps] (1.8MB )  

Machine Learning: Layered Learning

• Overlapping Layered Learning.
  Patrick MacAlpine and Peter Stone.
  Artificial Intelligence, 254:21–43, Elsevier, January 2018.
  Official version from Publisher's Webpage
  Accompanying videos at http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/overlappingLayeredLearning.html
  Details     BibTeX     Download: [pdf] (1.2MB )  [ps] (3.8MB )  

• A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer.
  David L. Leottau, Javier Ruiz-del-Solar, Patrick MacAlpine, and Peter Stone.
  In Luis Almeida, Jianmin Ji, Gerald Steinbauer, and Sean Luke, editors, RoboCup-2015: Robot Soccer World Cup XIX, Lecture Notes in Artificial Intelligence, Springer Verlag, Berlin, 2016.
  Details     BibTeX     Download: [pdf] (625.8kB )  

• UT Austin Villa 2014: RoboCup 3D Simulation League Champion via Overlapping Layered Learning.
  Patrick MacAlpine, Mike Depinet, and Peter Stone.
  In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), pp. 2842–48, January 2015.
  Accompanying videos at http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2014/html/overlappingLayeredLearning.html
  Details     BibTeX     Download: [pdf] (714.7kB )  [ps] (2.5MB )  [slides.pdf] (105.3MB )  

• Evolving Keepaway Soccer Players through Task Decomposition.
  Shimon Whiteson, Nate Kohl, Risto Miikkulainen, and Peter Stone.
  Machine Learning, 59(1):5–30, May 2005.
  Some videos of the agents before and after learning referenced in the paper.
  The publisher's official version
  An earlier version appeared in the proceedings of The Genetic and Evolutionary Computation Conference 2003 (GECCO-2003)
  Details     BibTeX     Download: [pdf] (278.8kB )  [ps] (566.8kB )  

• Concurrent Layered Learning.
  Shimon Whiteson and Peter Stone.
  In Second International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 193–200, ACM Press, New York, NY, July 2003.
  AAMAS-2003
  Details     BibTeX     Download: [pdf] (159.1kB )  [ps] (372.6kB )  

• Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer,
  Peter Stone.
  MIT Press, 2000.
  A book based on my Ph.D. thesis
  Contents, availability, and on-line appendices
  ISBN: 0262194384
  Details     BibTeX     Download: (unavailable)

• Layered Learning.
  Peter Stone and Manuela Veloso.
  In Ramon López de Mántaras and Enric Plaza, editors, Machine Learning: ECML 2000 (Proceedings of the Eleventh European Conference on Machine Learning), pp. 369–381, Springer Verlag, Barcelona,Catalonia,Spain, May/June 2000.
  Details     BibTeX     Download: [pdf] (152.3kB )  [ps] (362.7kB )  

• A Layered Approach to Learning Client Behaviors in the RoboCup Soccer Server.
  Peter Stone and Manuela Veloso.
  Applied Artificial Intelligence, 12:165–188, 1998.
  HTML version.
  Details     BibTeX     Download: [pdf] (189.1kB )  [ps] (1.2MB )  

Machine Learning: Markov Decision Processes

• Reinforcement Learning within the Classical Robotics Stack: A Case Study in Robot Soccer.
  Adam Labiosa, Zhihan Wang, Siddhant Agarwal, William Cong, Geethika Hemkumar, Abhinav Narayan Harish, Benjamin Hong, Josh Kelle, Chen Li, Yuhao Li, Zisen Shao, Peter Stone, and Josiah Hanna.
  In International Conference on Robotics and Automation (ICRA), May 2025.
  Details     BibTeX     Download: [pdf] (1.1MB )  

• Learning Optimal Advantage from Preferences and Mistaking it for Reward.
  W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson, Serena Booth, Anca Dragan, Peter Stone, and Scott Niekum.
  In The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), February 2024.
  Details     BibTeX     Download: [pdf] (3.6MB )  [slides.pdf] (3.9MB )  [poster.pdf] (2.9MB )  

• Models of human preference for learning reward functions.
  W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, and Alessandro Allievi.
  Transactions on Machine Learning Research (TMLR), 2023.
  Details     BibTeX     Download: [pdf] (6.7MB )  [slides.pdf] (13.4MB )  

• Reducing Sampling Error in Batch Temporal Difference Learning.
  Brahma Pavse, Ishan Durugkar, Josiah Hanna, and Peter Stone.
  In Proceedings of the 37th International Conference on Machine Learning (ICML), July 2020.
  The paper and talk is available from the ICML 2020 virtual conference page.
  Details     BibTeX     Download: [pdf] (738.4kB )  [slides.pdf] (5.2MB )  

• On Sampling Error in Batch Action-Value Prediction Algorithms.
  Brahma S. Pavse, Josiah P. Hanna, Ishan Durugkar, and Peter Stone.
  In In the Offline Reinforcement Learning Workshop at Neural Information Processing Systems (NeurIPS), December 2020., December 2020.
  5-minute Video Presentation
  Details     BibTeX     Download: [pdf] (327.2kB )  

• Building Self-Play Curricula Online by Playing with Expert Agents in Adversarial Games.
  Felipe Leno Da Silva, Anna Helena Reali Costa, and Peter Stone.
  In Proceedings of the 8th Brazilian Conference on Intelligent Systems (BRACIS), October 2019.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• On the Analysis of Complex Backup Strategies in Monte Carlo Tree Search.
  Piyush Khandelwal, Elad Liebman, Scott Niekum, and Peter Stone.
  In Proceedings of The 33rd International Conference on Machine Learning, pp. 1319–1328, June 2016.
  Details     BibTeX     Download: [pdf] (1.3MB )  [slides.pdf] (1.7MB )  

• Deep Recurrent Q-Learning for Partially Observable MDPs.
  Matthew Hausknecht and Peter Stone.
  In AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (AAAI-SDMIA15), November 2015.
  Details     BibTeX     Download: [pdf] (1.5MB )  [slides.pdf] (3.8MB )  

• Leading the Way: An Efficient Multi-robot Guidance System.
  Piyush Khandelwal, Samuel Barrett, and Peter Stone.
  In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
  Accompanying videos at https://www.youtube.com/watch?v=os1BjHgM5ao&feature=youtu.be
  Details     BibTeX     Download: [pdf] (694.2kB )  

• Framing reinforcement learning from human reward: Reward positivity, temporal discounting, episodicity, and performance.
  W. Bradley Knox and Peter Stone.
  Artificial Intelligence, 225(), August 2015.
  Artificial Intelligence
  Contains material that was previously published in a IUI 2013 paper and a RoMan 2012 paper that was nominated as a CoTeSys Cognitive Robotics BEST PAPER AWARD FINALIST.
  Details     BibTeX     Download: [pdf] (3.9MB )  

• Learning Complementary Multiagent Behaviors: A Case Study.
  Shivaram Kalyanakrishnan and Peter Stone.
  In Jacky Baltes, Michail G. Lagoudakis, Tadashi Naruse, and Saeed Shiry Ghidary, editors, RoboCup 2009: Robot Soccer World Cup XIII, pp. 153–165, Springer Verlag, 2010.
  BEST STUDENT PAPER AWARD WINNER at RoboCup International Symposium.
  Some simulations referenced in the paper.
  Details     BibTeX     Download: [pdf] (200.5kB )  [ps] (1.4MB )  

• State Abstraction Discovery from Irrelevant State Variables.
  Nicholas K. Jong and Peter Stone.
  In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, pp. 752–757, August 2005.
  Details     BibTeX     Download: [pdf] (102.1kB )  [ps] (249.8kB )  [slides.pdf] (323.4kB )  

• Bayesian Models of Nonstationary Markov Decision Problems.
  Nicholas K. Jong and Peter Stone.
  In IJCAI 2005 workshop on Planning and Learning in A Priori Unknown or Dynamic Domains, August 2005.
  Workshop webpage.
  Details     BibTeX     Download: [pdf] (42.4kB )  

• Improving Action Selection in MDP's via Knowledge Transfer.
  Alexander A. Sherstov and Peter Stone.
  In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
  AAAI 2005
  Details     BibTeX     Download: [pdf] (205.5kB )  [ps] (774.2kB )  

Machine Learning: NeuroEvolution

• Baby Mesh Recovery Enables Infant Behavior Identification.
  Charles Dardaman, Zifan Xu, Peter Stone, Karen Adolph, Danyang Han, and Georgios Pavlakos.
  In The IEEE International Conference on Development and Learning (ICDL), September 2026.
  Details     BibTeX     Download: [pdf] (8.0MB )  

• The Impact of Determinism on Learning Atari 2600 Games.
  Matthew Hausknecht and Peter Stone.
  In AAAI Workshop on Learning for General Competency in Video Games, January 2015.
  Details     BibTeX     Download: [pdf] (65.1kB )  

• A Neuroevolution Approach to General Atari Game Playing.
  Matthew Hausknecht, Joel Lehman, Risto Miikkulainen, and Peter Stone.
  IEEE Transactions on Computational Intelligence and AI in Games, 2014.
  Details     BibTeX     Download: [pdf] (1.3MB )  [ps] (3.1MB )  

• Comparing Evolutionary and Temporal Difference Methods for Reinforcement Learning.
  Matthew Taylor, Shimon Whiteson, and Peter Stone.
  In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1321–28, July 2006.
  BEST PAPER AWARD at GECCO 2006
  Details     BibTeX     Download: [pdf] (235.9kB )  [ps] (562.2kB )  

• Sample-Efficient Evolutionary Function Approximation for Reinforcement Learning.
  Shimon Whiteson and Peter Stone.
  In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 518–23, July 2006.
  AAAI 2006
  Details     BibTeX     Download: [pdf] (316.7kB )  [ps] (2.7MB )  

• On-Line Evolutionary Computation for Reinforcement Learning in Stochastic Domains.
  Shimon Whiteson and Peter Stone.
  In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1577–84, July 2006.
  GECCO 2006
  Details     BibTeX     Download: [pdf] (754.2kB )  [ps] (1.4MB )  

• Automatic Feature Selection via Neuroevolution.
  Shimon Whiteson, Peter Stone, Kenneth O. Stanley, Risto Miikkulainen, and Nate Kohl.
  In Proceedings of the Genetic and Evolutionary Computation Conference, June 2005.
  Details     BibTeX     Download: [pdf] (181.4kB )  [ps] (1.6MB )  

Machine Learning: Predictive State Representations

• Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference-Aligned Path Planning.
  Haresh, Karnan; Elvin, Yang; Garrett, Warnell; Joydeep, Biswas; Peter, and Stone.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (4.3MB )  

• Towards Employing PSRs in a Continuous Domain.
  Nicholas K. Jong and Peter Stone.
  Technical Report UT-AI-TR-04-309, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2004.
  UTAustin AI Lab technical reports
  Details     BibTeX     Download: (unavailable)

• Learning Predictive State Representations.
  Satinder Singh, Michael L. Littman, Nicholas K. Jong, David Pardoe, and Peter Stone.
  In Proceedings of the Twentieth International Conference on Machine Learning, August 2003.
  ICML-2003
  Details     BibTeX     Download: [pdf] (316.3kB )  [ps] (319.2kB )  

Machine Learning: Reinforcement Learning

• VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pokémon.
  Cameron Angliss, Jiaxun Cui, Jiaheng Hu, Arrasy Rahman, and Peter Stone.
  In International Conference on Autonomous Agents and Multiagent Systems, May 2026.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• Coachable agents for interactive gameplay.
  Roberto Capobianco, Harm van Seijen, Nolan D. Bard, Neil Burch, Fatima Davelouis, Josh Davidson, Alisa Devlic, Yunshu Du, Ishan Durugkar, Siddhant Gangapurwala, Daniel Hernandez, G. Zacharias Holland, Sahil Jain, Kenta Kawamoto, Raksha Kumaraswamy, Patrick MacAlpine, Dustin R. Morrill, Declan Oller, Francesco Riccio, Akanksha Saran, Craig Sherstan, Kaushik Subramanian, Thomas J. Walsh, Samuel Barrett, Kizza N. Frisbee, Mady Govil, Johannes Günther, Varun R. Kompella, James A. MacGlashan, Maxwell Svetlik, Michael D. Thomure, Jaden B. Travnik, Kevin Waugh, Elahe Aghapour, Florian Fuchs, Andreanne Lemay, Shruti Mishra, Takuma Seno, Peter Stone, Michael Spranger, and Peter R. Wurman.
  Technical Report arXiv e-Prints 2607.00642, arXiv, 2026.
  arXiv version
  Details     BibTeX     Download: [pdf] (9.1MB )  

• Out-of-Distribution Generalization with a SPARC: Racing 100 Unseen Vehicles with a Single Policy.
  Bram Grooten, Patrick MacAlpine, Kaushik Subramanian, Peter Stone, and Peter R. Wurman.
  In The AAAI Conference on Artificial Intelligence, January 2026.
  short video description; 10-minute talk
  Details     BibTeX     Download: [pdf] (3.9MB )  

• Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning.
  Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, and Roberto Martin-Martin.
  Reinforcement Learning Journal, 7, August 2026.
  Official version
  Details     BibTeX     Download: [pdf] (7.9MB )  

• Factored Latent Action World Models.
  Zizhao Wang, Chang Shi, Jiaheng Hu, Kevin Rohling, Roberto Martín-Martín, Amy Zhang, and Peter Stone .
  In International Conference on Machine Learning, July 2026.
  Details     BibTeX     Download: [pdf] (6.3MB )  

• Proto Successor Measure: Representing the Behavior Space of an RL Agent.
  Siddhant Agarwal, Harshit Sikchi, Peter Stone, and Amy Zhang.
  In International Conference on Machine Learning, June 2025.
  Details     BibTeX     Download: [pdf] (911.1kB )  

• MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention.
  Yuxin Chen, Chen Tang, Jianglan Wei, Chenran Li, Ran Tian, Xiang Zhang, Wei Zhan, Peter Stone, and Masayoshi Tomizuka.
  In Conference on Robot Learning (CoRL), September 2025.
  Details     BibTeX     Download: [pdf] (18.9MB )  

• SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL.
  Jiaheng Hu, Peter Stone, and Roberto Martín-Martín.
  In Conference on Robot Learning (CoRL), September 2025.
  Details     BibTeX     Download: [pdf] (2.6MB )  

• FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning.
  Jiaheng Hu, Rose Hendrix, Ali Farhadi, Aniruddha Kembhavi, Roberto Martín-Martín, Peter Stone, Kuo-Hao Zeng, and Kiana Ehsani.
  In ICRA, May 2025.
  Details     BibTeX     Download: [pdf] (9.2MB )  

• Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks.
  Viraj Joshi, Zifan Xu, Bo Liu, Peter Stone, and Amy Zhang.
  Reinforcement Learning Journal, 6:1293–317, August 2025.
  Official version
  Details     BibTeX     Download: [pdf] (3.5MB )  

• Reinforcement Learning within the Classical Robotics Stack: A Case Study in Robot Soccer.
  Adam Labiosa, Zhihan Wang, Siddhant Agarwal, William Cong, Geethika Hemkumar, Abhinav Narayan Harish, Benjamin Hong, Josh Kelle, Chen Li, Yuhao Li, Zisen Shao, Peter Stone, and Josiah Hanna.
  In International Conference on Robotics and Automation (ICRA), May 2025.
  Details     BibTeX     Download: [pdf] (1.1MB )  

• A Champion-Level Vision-Based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7.
  Hojoon Lee, Takuma Seno, Jun Jet Tai, Kaushik Subramanian, Kenta Kawamoto, Peter Stone, and Peter R. Wurman.
  IEEE Robotics and Automation Letters, 10(6):5545–52, June 2025.
  2.5-minute Video Summary
  Details     BibTeX     Download: [pdf] (2.1MB )  

• Hyperspherical Normalization for Scalable Deep Reinforcement Learning.
  Hojoon Lee, Youngdo Lee, Takuma Seno, Donghu Kim, Peter Stone, and Jaegul Choo.
  In International Conference on Machine Learning, June 2025.
  Details     BibTeX     Download: [pdf] (1.9MB )  

• SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning.
  Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, and Takuma Seno.
  In International Conference on Learning Representations, April 2025.
  Code and videos are on the paper's webpage
  Details     BibTeX     Download: [pdf] (5.7MB )  

• Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets.
  Alexander Levine, Peter Stone, and and Amy Zhang.
  Reinforcement Learning Journal, 6:2452–84, August 2025.
  Official version
  Details     BibTeX     Download: [pdf] (770.2kB )  

• Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory.
  Alexander Levine, Peter Stone, and Amy Zhang.
  In International Conference on Learning Representations, April 2025.
  Details     BibTeX     Download: [pdf] (1.1MB )  [slides.pdf] (1.6MB )  [poster.pdf] (3.4MB )  

• ProtoCRL: Prototype-based Network for Continual Reinforcement Learning.
  Michela Proietti, Peter R. Wurman, Peter Stone, and Roberto Capobianco.
  Reinforcement Learning Journal, 6:1624–46, August 2025.
  Official version
  Details     BibTeX     Download: [pdf] (2.1MB )  

• RLZero: Direct Policy Inference from Language Without In-Domain Supervision.
  Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo, Samyak Parajuli, Caleb Chuck, Max Rudolph, Peter Stone, Amy Zhang, and Scott Niekum.
  In Conference on Neural Information Processing Systems (NeurIPS, December 2025.
  Details     BibTeX     Download: [pdf] (1.7MB )  

• Dyn-O: Building Structured World Models with Object-Centric Representations.
  Zizhao Wang, Kaixin Wang, Li Zhao, Peter Stone, and Jiang Bian.
  In Conference on Neural Information Processing Systems (NeurIPS), December 2025.
  Details     BibTeX     Download: [pdf] (22.9MB )  

• GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring.
  Linji Wang, Zifan Xu, Peter Stone, and Xuesu Xiao.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2025.
  Details     BibTeX     Download: [pdf] (1.8MB )  

• Learning to Look: Seeking Information for Decision Making via Policy Factorization.
  Shivin Dass, Jiaheng Hu, Ben Abbatematteo, Peter Stone, and Roberto Martín-Martín.
  In Conference on Robot Learning (CoRL), November 2024.
  Details     BibTeX     Download: [pdf] (6.8MB )  

• Data-Efficient Policy Evaluation Through Behavior Policy Search.
  Josiah P. Hanna, Yash Chandak, Philip S. Thomas, Martha White, Peter Stone, and Scott Niekum.
  Journal of Machine Learning Research, 2024.
  Official version on publisher's website
  Details     BibTeX     Download: [pdf] (1.7MB )  

• Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning.
  Jiaheng Hu, Zizhao Wang, Roberto Martín-Martín, and Peter Stone.
  In Conference on Neural Information Parocessing Systems (NeurIPS), December 2024.
  Details     BibTeX     Download: [pdf] (2.7MB )  

• Discovering Creative Behaviors through DUPLEX: Diverse Universal Features for Policy Exploration.
  Borja G. Leon, Francesco Riccio, Kaushik Subramanian, and Peter R. Wurman an Peter Stone.
  In Conference on Neural Information Processing Systems (NeurIPS), December 2024.
  Project website (with videos)
  Details     BibTeX     Download: [pdf] (1.5MB )  [slides.pdf] (1.4MB )  [poster.pdf] (1.5MB )  

• Multistep Inverse Is Not All You Need.
  Alexander Levine, Peter Stone, and Amy Zhang.
  Reinforcement Learning Journal, 2:884–925, 2024.
  Official version
  Details     BibTeX     Download: [pdf] (1.8MB )  [slides.pdf] (4.8MB )  [poster.pdf] (2.7MB )  

• Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents.
  Arrasy Rahman, Jiaxun Cui, and Peter Stone.
  In AAAI, February 2024.
  the conference presentation
  Details     BibTeX     Download: [pdf] (1.3MB )  [slides.ppt] (9.2MB )  [poster.pdf] (956.2kB )  

• Relaxed Exploration Constrained Reinforcement Learning.
  Shahaf S. Shperberg, Bo Liu, and Peter Stone.
  In Conference on Autonomous Agents and Multiagent Systems, May 2024.
  Details     BibTeX     Download: [pdf] (3.4MB )  

• Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes.
  Chen Tang, Ben Abbatematteo, Jiaheng Hu, Rohan Chandra, Roberto Martín-Martín, and Peter Stone.
  Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), 8:153–88, 2024.
  Presented in Senior member track at AAAI 2025
  Official version on publisher's website
  Details     BibTeX     Download: [pdf] (4.3MB )  [slides.pdf] (2.9MB )  [poster.pdf] (604.3kB )  

• SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions.
  Zizhao Wang, Jiaheng Hu, Caleb Chuck, Stephen Chen, Roberto Martín-Martín, Amy Zhang, Scott Niekum, and Peter Stone.
  In Conference on Neural Information Processing Systems (NeurIPS), December 2024.
  Details     BibTeX     Download: [pdf] (2.1MB )  

• N-Agent Ad Hoc Teamwork.
  Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman, and Peter Stone.
  In Conference on Neural Information Processing Systems (NeurIPS), December 2024.
  Details     BibTeX     Download: [pdf] (1.6MB )  [slides.pdf] (1.3MB )  [poster.pdf] (1.7MB )  

• Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning.
  Zizhao Wang, Caroline Wang, Xuesu Xiao, Yuke Zhu, and Peter Stone.
  In AAAI Conference on Artificial Intelligence, February 2024.
  Details     BibTeX     Download: [pdf] (4.5MB )  

• Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks.
  Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, and Ambuj Tewari.
  In International Conference on Learning Representations (ICLR), May 2024.
  Details     BibTeX     Download: [pdf] (1.4MB )  

• Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning.
  Zifan Xu, Amir Hossain Raj, Xuesu Xiao, and Peter Stone.
  In IEEE International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences.
  Siddhant Agarwal, Ishan Durugkar, Peter Stone, and Amy Zhang.
  In Conference on Neural Information Processing Systems, December 2023.
  Details     BibTeX     Download: [pdf] (3.4MB )  [slides.pptx] (13.6MB )  [poster.pdf] (1.8MB )  

• Task Phasing: Automated Curriculum Learning from Demonstrations.
  Vaibhav Bajaj, Guni Sharon, and Peter Stone.
  In Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS 2023), July 2023.
  Accompanying code
  Details     BibTeX     Download: [pdf] (416.0kB )  

• The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications.
  Serena Booth, W Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, and Alessandro Allievi.
  In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), Feb 2023.
  Project page with slides and video.
  Details     BibTeX     Download: [pdf] (1.4MB )  

• MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection.
  Jiaxun Cui, Xiaomeng Yang, Mulong Luo, Geunbae Lee, Peter Stone, Hsien-Hsin S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong, and Yuandong Tian.
  In The Eleventh International Conference on Learning Representations (ICLR), May 2023.
  Video presentation
  Details     BibTeX     Download: [pdf] (769.8kB )  [slides.pdf] (2.1MB )  [poster.pdf] (1.8MB )  

• Causal Policy Gradient for Whole-Body Mobile Manipulation.
  Jiaheng Hu, Peter Stone, and Roberto Martin-Martin.
  In Robotics: Science and Systems (RSS), July 2023.
  Details     BibTeX     Download: [pdf] (4.0MB )  

• Models of human preference for learning reward functions.
  W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, and Alessandro Allievi.
  Transactions on Machine Learning Research (TMLR), 2023.
  Details     BibTeX     Download: [pdf] (6.7MB )  [slides.pdf] (13.4MB )  

• Reward (Mis)design for Autonomous Driving.
  W. Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone.
  Artificial Intelligence, 316:103829, 2023.
  Paper webpage
  Details     BibTeX     Download: [pdf] (696.3kB )  [ps] (5.6MB )  

• Event Tables for Efficient Experience Replay.
  Varun Kompella, Thomas Walsh, Samuel Barrett, Peter Wurman, and Peter Stone.
  Transactions on Machine Learning Research (TMLR), 2023.
  Details     BibTeX     Download: [pdf] (6.8MB )  

• Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning.
  Bo Liu, Yihao Feng, Qiang Liu, and Peter Stone.
  In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), Februray 2023.
  Details     BibTeX     Download: [pdf] (1.8MB )  

• ELDEN: Exploration via Local Dependencies.
  Zizhao Wang, Jiaheng Hu, Peter Stone, and Roberto Martín-Martín.
  In Conference on Neural Information Processing Systems, December 2023.
  Details     BibTeX     Download: [pdf] (6.2MB )  [slides.pptx] (22.4MB )  [poster.pdf] (856.5kB )  

• D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning.
  Caroline Wang, Garrett Warnell, and Peter Stone.
  In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2023.
  Details     BibTeX     Download: [pdf] (1.6MB )  [slides.pdf] (2.4MB )  [poster.pdf] (1.4MB )  

• DM$^2$: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching.
  Caroline Wang, Ishan Durugkar, Elad Liebman, and Peter Stone.
  In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), February 2023.
  Details     BibTeX     Download: [pdf] (801.2kB )  [slides.pdf] (3.7MB )  [poster.pdf] (1.4MB )  

• Model-Based Meta Automatic Curriculum Learning.
  Zifan Xu, Yulin Zhang, Shahaf S. Shperberg, Reuth Mirsky, Yuqian Jiang, Bo Liu, and Peter Stone.
  In The Second Conference on Lifelong Learning Agents (CoLLAs), August 2023.
  Video presentation
  Details     BibTeX     Download: [pdf] (1.0MB )  [slides.pptx] (6.6MB )  

• Learning a robust multiagent driving policy for traffic congestion reduction.
  Yulin Zhang, William Macke, Jiaxun Cui, Sharon Hornstein, Daniel Urieli, and Peter Stone.
  Neural Computing and Applications, 2023.
  Details     BibTeX     Download: [pdf] (1.2MB )  

• Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch.
  Eddy Hudson, Garrett Warnell, Faraz Torabi, and Peter Stone.
  In International Conference on Robotics and Automation (ICRA), May 2022.
  Presentation Video
  Details     BibTeX     Download: [pdf] (2.5MB )  

• VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation.
  Haresh Karnan, Garrett Warnell, Xuesu Xiao, and Peter Stone.
  In International Conference on Robotics and Automation, 2022, May 2022.
  Poster, Video
  Details     BibTeX     Download: [pdf] (1.5MB )  

• Adversarial Imitation Learning from Video using a State Observer.
  Haresh Karnan, Garrett Warnell, Faraz Torabi, and Peter Stone.
  In International Conference on Robotics and Automation, 2022, May 2022.
  Video
  Details     BibTeX     Download: [pdf] (933.2kB )  

• Value Function Decomposition for Iterative Design of Reinforcement Learning Agents.
  James MacGlashan, Evan Archer, Alisa Devlic, Takuma Seno, Craig Sherstan, Peter R. Wurman, and Peter Stone.
  In Conference on Neural Information Processing Systems (NeurIPS), December 2022.
  5-minute Video Presentation; the slides
  Details     BibTeX     Download: [pdf] (11.6MB )  

• Task Factorization in Curriculum Learning.
  Reuth Mirsky, Shahaf S. Shperberg, Yulin Zhang, Zifan Xu, Yuqian Jiang, Jiaxun Cui, and Peter Stone.
  In ICML workshop on Decision Awareness in Reinforcement Learning (DARL), July 2022.
  recorded presentation
  Details     BibTeX     Download: [pdf] (1011.6kB )  

• Causal Dynamics Learning for Task-Independent State Abstraction.
  Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, and Peter Stone.
  In Proceedings of the 39th International Conference on Machine Learning (ICML2022), July 2022.
  recorded presentation
  Details     BibTeX     Download: [pdf] (4.2MB )  [slides.pdf] (4.0MB )  [poster.pdf] (1.6MB )  

• Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning.
  Peter R. Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Varun Kompella, Piyush Khandelwal, HaoChih Lin, Patrick MacAlpine, Declan Oller, Craig Sherstan, Takuma Seno, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead, Peter Duerr, Peter Stone, Michael Spranger, and and Hiroaki Kitano.
  Nature, 62:223–28, Feb. 2022.
  Available from Nature website.
  project webpage
  Details     BibTeX     Download: [pdf] (2.0MB )  

• Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy.
  Yunshu Du, Garrett Warnell, Assefaw Gebremedhin, Peter Stone, and Matthew E. Taylor.
  Neural Computing and Applications, May 2021.
  Details     BibTeX     Download: [pdf] (2.2MB )  

• Adversarial Intrinsic Motivation for Reinforcement Learning.
  Ishan Durugkar, Mauricio Tec, Scott Niekum, and Peter Stone.
  In Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021), December 2021.
  slides and video presentation
  Details     BibTeX     Download: [pdf] (5.1MB )  

• Capturing Skill State in Curriculum Learning for Human Skill Acquisition.
  Keya Ghonasgi, Reuth Mirsky, Sanmit Narvekar, Bharath Masetty, Adrian M. Haith, Peter Stone, and Ashish D. Deshpande.
  In International Conference on Intelligent Robots and Systems (IROS), September 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Grounded Action Transformation for Sim-to-Real Reinforcement Learning.
  Josiah P. Hanna, Siddharth Desai, Haresh Karnan, Garrett Warnell, and Peter Stone.
  Special Issue on Reinforcement Learning for Real Life, Machine Learning, 2021, May 2021.
  Details     BibTeX     Download: [pdf] (3.0MB )  

• Importance Sampling in Reinforcement Learning with an Estimated Behavior Policy.
  Josiah P. Hanna, Scott Niekum, and Peter Stone.
  Machine Learning (MLJ), 110:1267–1317, May 2021.
  Details     BibTeX     Download: [pdf] (3.7MB )  

• RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning.
  Eddy Hudson, Garrett Warnell, and Peter Stone.
  In Autonomous Robots and Multirobot Systems Workshop at the 20th International Conference onAutonomous Agents and Multiagent Systems (AAMAS 2021), May 2021.
  Details     BibTeX     Download: [pdf] (393.0kB )  

• Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks.
  Yuqian Jiang, Suda Bharadwaj, Bo Wu, Rishi Shah, Ufuk Topcu, and Peter Stone.
  In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), February 2021.
  Details     BibTeX     Download: [pdf] (1.8MB )  [slides.pdf] (1.8MB )  

• Policy Evaluation in Continuous MDPs with Efficient Kernelized Gradient Temporal Difference.
  Alec Koppel, Garrett Warnell, Ethan Stump, Peter Stone, and Alejandro Ribeiro.
  IEEE Transactions on Automatic Control, 66(4):1856–63, April 2021.
  official online version
  Details     BibTeX     Download: [pdf] (648.8kB )  

• Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition.
  Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, and Animashree Anandkumar.
  In Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021 (ICML), July 2021.
  Details     BibTeX     Download: [pdf] (2.2MB )  [poster.pdf] (1.1MB )  

• Is the Cerebellum a Model-Based Reinforcement Learning Agent?.
  Bharath Masetty, Reuth Mirsky, Ashish D. Deshpande, Michael Mauk, and Peter Stone.
  In Adaptive and Learning Agents Workshop at AAMAS, May 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (594.9kB )  [slides.pdf] (1.5MB )  

• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation.
  Faraz Torabi, Garrett Warnell, and Peter Stone.
  In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (742.7kB )  

• A Scavenger Hunt for Service Robots.
  Harel Yedidsion, Jennifer Suriadinata, Zifan Xu, Stefan Debruyn, and Peter Stone.
  In Proceedings of the 2021 International Conference on Robotics and Automation (ICRA 2021), May 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (1.5MB )  

• The EMPATHIC Framework for Task Learning from Implicit Human Feedback.
  Yuchen Cui, Qiping Zhang, Alessandro Allievi, Peter Stone, Scott Niekum, and W. Bradley Knox.
  In Proceedings of the 4th Conference on Robot Learning (CoRL 2020), November 2020.
  5-minute video presentation; 47-minute in-depth talk.
  Project website.
  Raw data from experiments.
  Details     BibTeX     Download: [pdf] (6.4MB )  [slides.pptx] (59.4MB )  

• An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch.
  Siddarth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, and Peter Stone.
  In Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS 2020), December 2020.
  Poster
  Details     BibTeX     Download: [pdf] (1.3MB )  

• Stochastic Grounded Action Transformation for Robot Learning in Simulation.
  Siddharth Desai, Haresh Karnan, Josiah P. Hanna, Garrett Warnell, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020), October 2020.
  11-minute video presentation.
  Details     BibTeX     Download: [pdf] (1.9MB )  

• Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning.
  Ishan Durugkar, Elad Liebman, and Peter Stone.
  In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), July 2020.
  Details     BibTeX     Download: [pdf] (3.9MB )  

• Reinforced Grounded Action Transformation for Sim-to-Real Transfer.
  Haresh Karnan, Siddharth Desai, Josiah P. Hanna, Garrett Warnell, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020), October 2020.
  14-minute video presentation.
  Details     BibTeX     Download: [pdf] (506.6kB )  

• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.
  Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, and Peter Stone.
  Journal of Machine Learning Research, 21(181):1–50, 2020.
  Details     BibTeX     Download: [pdf] (1.4MB )  

• Generalizing Curricula for Reinforcement Learning.
  Sanmit Narvekar and Peter Stone.
  In 4th Lifelong Learning Workshop at the International Conference on Machine Learning (ICML 2020), July 2020.
  Details     BibTeX     Download: [pdf] (330.4kB )  [slides.pdf] (3.8MB )  

• RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration.
  Brahma Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), 5:6262–69, October 2020.
  Video of the experiments; 13-minute video presentation.
  Details     BibTeX     Download: [pdf] (405.1kB )  [slides.pptx] (115.4MB )  

• Reducing Sampling Error in Batch Temporal Difference Learning.
  Brahma Pavse, Ishan Durugkar, Josiah Hanna, and Peter Stone.
  In Proceedings of the 37th International Conference on Machine Learning (ICML), July 2020.
  The paper and talk is available from the ICML 2020 virtual conference page.
  Details     BibTeX     Download: [pdf] (738.4kB )  [slides.pdf] (5.2MB )  

• On Sampling Error in Batch Action-Value Prediction Algorithms.
  Brahma S. Pavse, Josiah P. Hanna, Ishan Durugkar, and Peter Stone.
  In In the Offline Reinforcement Learning Workshop at Neural Information Processing Systems (NeurIPS), December 2020., December 2020.
  5-minute Video Presentation
  Details     BibTeX     Download: [pdf] (327.2kB )  

• Deep R-Learning for Continual Area Sweeping.
  Rishi Shah, Yuqian Jiang, Justin Hart, and Peter Stone.
  In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020), October 2020.
  1-minute video demonstration; 13-minute Video presentation.
  Details     BibTeX     Download: [pdf] (374.2kB )  [slides.pdf] (1.1MB )  

• Importance Sampling Policy Evaluation with an Estimated Behavior Policy.
  Josiah Hanna, Scott Niekum, and Peter Stone.
  In Proceedings of the 36th International Conference on Machine Learning (ICML), June 2019.
  Details     BibTeX     Download: [pdf] (2.7MB )  [slides.pdf] (4.0MB )  

• Reducing Sampling Error in Policy Gradient Learning.
  Josiah Hanna and Peter Stone.
  In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2019.
  This paper contains material that was previously presented at the 2018 NeurIPS Deep Reinforcement Learning Workshop.
  Details     BibTeX     Download: [pdf] (1.5MB )  [slides.pdf] (3.1MB )  

• The right music at the right time: adaptive personalized playlists based on sequence modeling.
  Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone Peter Stone.
  Management Information Systems Quarterly, 43(3):765–786, Society for Information Management and The Management Information Systems Research Center, 2019.
  Available from publisher's website.
  Details     BibTeX     Download: [pdf] (4.0MB )  

• Learning Curriculum Policies for Reinforcement Learning.
  Sanmit Narvekar and Peter Stone.
  In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2019.
  Details     BibTeX     Download: [pdf] (953.0kB )  [slides.pdf] (5.6MB )  

• Agents teaching agents: a survey on inter-agent transfer learning.
  Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone.
  Autonomous Agents and Multi-Agent Systems, Dec 2019.
  Official version from JAAMAS
  Details     BibTeX     Download: [pdf] (572.4kB )  

• Building Self-Play Curricula Online by Playing with Expert Agents in Adversarial Games.
  Felipe Leno Da Silva, Anna Helena Reali Costa, and Peter Stone.
  In Proceedings of the 8th Brazilian Conference on Intelligent Systems (BRACIS), October 2019.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Leveraging Human Guidance for Deep Reinforcement Learning Tasks.
  Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. Ballard, and Peter Stone.
  In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), August 2019.
  Details     BibTeX     Download: [pdf] (471.1kB )  [slides.pdf] (1.2MB )  

• Towards a Data Efficient Off-Policy Policy Gradient.
  Josiah Hanna and Peter Stone.
  In AAAI Spring Symposium on Data Efficient Reinforcement Learning, March 2018.
  Details     BibTeX     Download: [pdf] (345.4kB )  

• A Stitch in Time - Autonomous Model Management via Reinforcement Learning.
  Elad Liebman, Eric Zavesky, and Peter Stone.
  In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), July 2018.
  Details     BibTeX     Download: [pdf] (1.7MB )  

• State Abstraction Synthesis for Discrete Models of Continuous Domains.
  Jacob Menashe and Peter Stone.
  In Data Efficient Reinforcement Learning Workshop at AAAI Spring Symposium, March 2018.
  Details     BibTeX     Download: [pdf] (538.3kB )  [ps] (5.3MB )  

• Deterministic Implementations for Reproducibility in Deep Reinforcement Learning.
  Prabhat Nagarajan, Garrett Warnell, and Peter Stone.
  In 2nd Reproducibility in Machine Learning Workshop at ICML 2018, July 2018.
  Details     BibTeX     Download: [pdf] (6.6MB )  

• Learning a Policy for Opportunistic Active Learning.
  Aishwarya Padmakumar, Peter Stone, and Raymond J. Mooney.
  In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.
  Details     BibTeX     Download: [pdf] (394.3kB )  

• Deep TAMER: Interactive agent shaping in high-dimensional state spaces.
  Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, and Peter Stone.
  In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, February 2018.
  Details     BibTeX     Download: [pdf] (1.6MB )  [slides.pptx] (16.2MB )  

• TD Learning with Constrained Gradients.
  Ishan Durugkar and Peter Stone.
  In Proceedings of the Deep Reinforcement Learning Symposium, NIPS 2017, December 2017.
  Details     BibTeX     Download: [pdf] (381.4kB )  

• Data-Efficient Policy Evaluation Through Behavior Policy Search.
  Josiah Hanna, Philip Thomas, Peter Stone, and Scott Niekum.
  In Proceedings of the 34th International Conference on Machine Learning (ICML), August 2017.
  Details     BibTeX     Download: [pdf] (1.2MB )  [slides.pdf] (1.1MB )  

• Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation.
  Josiah Hanna, Peter Stone, and Scott Niekum.
  In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2017.
  Details     BibTeX     Download: [pdf] (663.8kB )  [ps] (572.6kB )  [slides.pdf] (1.3MB )  

• Intrinsically motivated model learning for developing curious robots.
  Todd Hester and Peter Stone.
  Artificial Intelligence, 247:170–86, June 2017.
  from journal website.
  Details     BibTeX     Download: [pdf] (1.4MB )  

• Designing Better Playlists with Monte Carlo Tree Search.
  Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, and Peter Stone.
  In Proceedings of the Twenty-Ninth Conference On Innovative Applications Of Artificial Intelligence (IAAI-17), February 2017.
  Details     BibTeX     Download: [pdf] (377.0kB )  

• Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning.
  Sanmit Narvekar, Jivko Sinapov, and Peter Stone.
  In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), August 2017.
  Details     BibTeX     Download: [pdf] (826.2kB )  [slides.pdf] (5.8MB )  

• Making Friends on the Fly: Cooperating with New Teammates.
  Samuel Barrett, Avi Rosenfeld, Sarit Kraus, and Peter Stone.
  Artificial Intelligence, October 2016.
  Official version from journal website.
  Details     BibTeX     Download: [pdf] (917.9kB )  

• State Aggregation through Reasoning in Answer Set Programming.
  Ginevra Gaudioso, Matteo Leonetti, and Peter Stone.
  In Proceedings of the IJCAI Workshop on Autonomous Mobile Service Robots (WSR 16), July 2016.
  Details     BibTeX     Download: [pdf] (776.6kB )  

• Deep Reinforcement Learning in Parameterized Action Space.
  Matthew Hausknecht and Peter Stone.
  In Proceedings of the International Conference on Learning Representations (ICLR), May 2016.
  Details     BibTeX     Download: [pdf] (468.3kB )  

• Grounded Semantic Networks for Learning Shared Communication Protocols.
  Matthew Hausknecht and Peter Stone.
  In Deep Reinforcement Learning, NIPS Workshop, December 2016.
  Details     BibTeX     Download: [pdf] (899.9kB )  

• On-Policy vs. Off-Policy Updates for Deep Reinforcement Learning.
  Matthew Hausknecht and Peter Stone.
  In Deep Reinforcement Learning: Frontiers and Challenges, IJCAI Workshop, July 2016.
  Details     BibTeX     Download: [pdf] (2.5MB )  

• Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork.
  Matthew Hausknecht, Prannoy Mupparaju, Sandeep Subramanian, Shivaram Kalyanakrishnan, and Peter Stone.
  In AAMAS Adaptive Learning Agents (ALA) Workshop, May 2016.
  Details     BibTeX     Download: [pdf] (253.9kB )  

• Deep Imitation Learning for Parameterized Action Spaces.
  Matthew Hausknecht, Yilun Chen, and Peter Stone.
  In AAMAS Adaptive Learning Agents (ALA) Workshop, May 2016.
  Details     BibTeX     Download: [pdf] (483.4kB )  

• A synthesis of automated planning and reinforcement learning for efficient, robust decision-making.
  Matteo Leonetti, Luca Iocchi, and Peter Stone.
  Artificial Intelligence, 241:103 – 130, September 2016.
  Details     BibTeX     Download: [pdf] (3.2MB )  

• Source Task Creation for Curriculum Learning.
  Sanmit Narvekar, Jivko Sinapov, Matteo Leonetti, and Peter Stone.
  In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), May 2016.
  Details     BibTeX     Download: [pdf] (630.0kB )  [slides.pdf] (10.2MB )  

• Deep Recurrent Q-Learning for Partially Observable MDPs.
  Matthew Hausknecht and Peter Stone.
  In AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (AAAI-SDMIA15), November 2015.
  Details     BibTeX     Download: [pdf] (1.5MB )  [slides.pdf] (3.8MB )  

• DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation.
  Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone.
  In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
  Details     BibTeX     Download: [pdf] (1.5MB )  [ps] (38.4MB )  [slides.pdf] (2.6MB )  

• Monte Carlo Hierarchical Model Learning.
  Jacob Menashe and Peter Stone.
  In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
  Details     BibTeX     Download: [pdf] (693.2kB )  [ps] (18.4MB )  

• Learning Inter-Task Transferability in the Absence of Target Task Samples.
  Jivko Sinapov, Sanmit Narvekar, Matteo Leonetti, and Peter Stone.
  In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015.
  Details     BibTeX     Download: [pdf] (337.2kB )  

• Communicating with Unknown Teammates.
  Samuel Barrett, Noa Agmon, Noam Hazon, Sarit Kraus, and Peter Stone.
  In Proceedings of the Twenty-First European Conference on Artificial Intelligence, August 2014.
  Details     BibTeX     Download: [pdf] (215.9kB )  [ps] (2.6MB )  [slides.pdf] (1.4MB )  

• TacTex'13: A Champion Adaptive Power Trading Agent.
  Daniel Urieli and Peter Stone.
  In Proceedings of the Twenty-Eighth Conference on Artificial Intelligence (AAAI 2014), July 2014.
  Details     BibTeX     Download: [pdf] (257.8kB )  [ps] (7.3MB )  [slides.pdf] (3.8MB )  

• Multiagent Learning in the Presence of Memory-Bounded Agents.
  Doran Chakraborty and Peter Stone.
  Autonomous Agents and Multiagent Systems (JAAMAS), Springer, 2013.
  Details     BibTeX     Download: [pdf] (676.7kB )  [ps] (447.9kB )  

• Cooperating with a Markovian Ad Hoc Teammate.
  Doran Chakraborty and Peter Stone.
  In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.
  Details     BibTeX     Download: [pdf] (202.3kB )  [ps] (408.2kB )  

• Targeted Opponent Modeling of Memory-Bounded Agents.
  Doran Chakraborty, Noa Agmon, and Peter Stone.
  In Proceedings of the Adaptive Learning Agents Workshop (ALA), May 2013.
  Details     BibTeX     Download: [pdf] (629.0kB )  [ps] (1.6MB )  

• TEXPLORE: Real-Time Sample-Efficient Reinforcement Learning for Robots.
  Todd Hester and Peter Stone.
  Machine Learning, 90(3):385–429, 2013.
  Official version from journal website.
  Details     BibTeX     Download: [pdf] (987.8kB )  [ps] (3.2MB )  

• The Open-Source TEXPLORE Code Release for Reinforcement Learning on Robots.
  Todd Hester and Peter Stone.
  In Sven Behnke, Arnoud Visser, Rong Xiong, and Manuela Veloso, editors, RoboCup-2013: Robot Soccer World Cup XVII, Lecture Notes in Artificial Intelligence, Springer Verlag, Berlin, 2013.
  Details     BibTeX     Download: [pdf] (234.7kB )  [ps] (2.9MB )  

• Learning Exploration Strategies in Model-Based Reinforcement Learning.
  Todd Hester, Manuel Lopes, and Peter Stone.
  In The Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.
  Details     BibTeX     Download: [pdf] (258.7kB )  [ps] (2.4MB )  

• Training a Robot via Human Feedback: A Case Study.
  W. Bradley Knox, Peter Stone, and Cynthia Breazeal.
  In International Conference on Social Robotics, October 2013.
  BEST PAPER AWARD WINNER at ICSR 2013
  An associated video summarizing the paper (direct link).
  Details     BibTeX     Download: [pdf] (2.3MB )  

• Model-Selection for Non-Parametric Function Approximation in Continuous Control Problems: A Case Study in a Smart Energy System.
  Daniel Urieli and Peter Stone.
  In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML'13), Sep 2013.
  Official publisher version
  Details     BibTeX     Download: [pdf] (2.3MB )  [ps] (1.4MB )  [slides.pdf] (3.6MB )  

• A Learning Agent for Heat-Pump Thermostat Control.
  Daniel Urieli and Peter Stone.
  In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.
  Details     BibTeX     Download: [pdf] (426.6kB )  [slides.pdf] (4.6MB )  

• RTMBA: A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control.
  Todd Hester, Michael Quinlan, and Peter Stone.
  In IEEE International Conference on Robotics and Automation (ICRA), May 2012.
  Details     BibTeX     Download: [pdf] (359.7kB )  [ps] (1.9MB )  

• How Humans Teach Agents: A New Experimental Perspective.
  W. Bradley Knox, Brian D. Glass, Bradley C. Love, W. Todd Maddox, and Peter Stone.
  International Journal of Social Robotics, 4:409–421, Springer Netherlands, October 2012. 10.1007/s12369-012-0163-x
  International Journal of Social Robotics
  Download article from publisher
  Details     BibTeX     Download: [pdf] (2.6MB )  

• The Nature of Belief-Directed Exploratory Choice in Human Decision-Making.
  W. Bradley Knox, A. Ross Otto, Peter Stone, and Bradley Love.
  Frontiers in Psychology, 2(398), January 2012.
  Frontiers in Psychology
  Download article from publisher (free)
  A follow-up commentary by Erica Yu.
  Details     BibTeX     Download: (unavailable)

• Reinforcement Learning from Simultaneous Human and MDP Reward.
  W. Bradley Knox and Peter Stone.
  In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), June 2012.
  AAMAS 2012
  Details     BibTeX     Download: [pdf] (929.6kB )  [ps] (4.9MB )  

• Learning from feedback on actions past and intended.
  W. Bradley Knox, Cynthia Breazeal, and Peter Stone.
  In Proceedings of 7th ACM/IEEE International Conference on Human-Robot Interaction, Late-Breaking Reports Session (HRI), March 2012.
  HRI 2012
  Details     BibTeX     Download: [pdf] (447.6kB )  [ps] (3.9MB )  

• Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree.
  Doran Chakraborty and Peter Stone.
  In Proceedings of the Twenty Eighth International Conference on Machine Learning (ICML), 2011.
  Details     BibTeX     Download: [pdf] (223.3kB )  [ps] (521.1kB )  

• Learning and Using Models.
  Todd Hester and Peter Stone.
  In Marco Wiering and Martijn van Otterlo, editors, Reinforcement Learning: State of the Art, Springer Verlag, Berlin, Germany, 2011.
  Details     BibTeX     Download: [pdf] (474.7kB )  [ps] (1.0MB )  

• Characterizing Reinforcement Learning Methods through Parameterized Learning Problems.
  Shivaram Kalyanakrishnan and Peter Stone.
  Machine Learning (MLJ), 84(1--2):205–247, July 2011.
  Publisher's on-line version
  Details     BibTeX     Download: [pdf] (858.5kB )  [ps] (2.4MB )  

• On Learning with Imperfect Representations.
  Shivaram Kalyanakrishnan and Peter Stone.
  In Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pp. 17–24, IEEE, April 2011.
  Details     BibTeX     Download: [pdf] (163.8kB )  [ps] (196.0kB )  

• Understanding Human Teaching Modalities in Reinforcement Learning Environments: A Preliminary Report.
  W. Bradley Knox and Peter Stone.
  In IJCAI 2011 Workshop on Agents Learning Interactively from Human Teachers (ALIHT), July 2011.
  IJCAI 2011 Workshop on Agents Learning Interactively from Human Teachers (ALIHT)
  Details     BibTeX     Download: [pdf] (372.4kB )  [ps] (41.6MB )  

• An Introduction to Inter-task Transfer for Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  AI Magazine, 32(1):15–34, 2011.
  Details     BibTeX     Download: [pdf] (237.0kB )  [ps] (773.0kB )  

• Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning.
  Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter Stone.
  In IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), April 2011.
  2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)
  Details     BibTeX     Download: [pdf] (165.3kB )  

• Transfer Learning for Reinforcement Learning on a Physical Robot.
  Samuel Barrett, Matt E. Taylor, and Peter Stone.
  In Ninth International Conference on Autonomous Agents and Multiagent Systems - Adaptive Learning Agents Workshop (AAMAS - ALA), May 2010.
  AAMAS ALA 2010
  Details     BibTeX     Download: [pdf] (688.8kB )  [ps] (5.7MB )  

• Convergence, Targeted Optimality and Safety in Multiagent Learning.
  Doran Chakraborty and Peter Stone.
  In Proceedings of the Twenty-seventh International Conference on Machine Learning (ICML), June 2010.
  Details     BibTeX     Download: [pdf] (196.9kB )  [ps] (474.1kB )  

• Real Time Targeted Exploration in Large Domains.
  Todd Hester and Peter Stone.
  In The Ninth International Conference on Development and Learning (ICDL), August 2010.
  ICDL 2010
  Details     BibTeX     Download: [pdf] (253.9kB )  [ps] (554.1kB )  

• Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration.
  Tobias Jung and Peter Stone.
  In The European Conference on Machine Learning (ECML), September 2010.
  Details     BibTeX     Download: [pdf] (417.0kB )  [ps] (6.5MB )  [slides.pdf] (505.6kB )  

• Learning Complementary Multiagent Behaviors: A Case Study.
  Shivaram Kalyanakrishnan and Peter Stone.
  In Jacky Baltes, Michail G. Lagoudakis, Tadashi Naruse, and Saeed Shiry Ghidary, editors, RoboCup 2009: Robot Soccer World Cup XIII, pp. 153–165, Springer Verlag, 2010.
  BEST STUDENT PAPER AWARD WINNER at RoboCup International Symposium.
  Some simulations referenced in the paper.
  Details     BibTeX     Download: [pdf] (200.5kB )  [ps] (1.4MB )  

• Combining Manual Feedback with Subsequent MDP Reward Signals for Reinforcement Learning.
  W. Bradley Knox and Peter Stone.
  In Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), May 2010.
  Winner of the Pragnesh Jay Modi BEST STUDENT PAPER AWARD (and best paper award nominee).
  The TAMER project page with videos of TAMER in action.
  AAMAS-2010
  Details     BibTeX     Download: [pdf] (422.9kB )  [ps] (3.5MB )  

• Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning.
  Shimon Whiteson, Matthew E. Taylor, and Peter Stone.
  Journal of Autonomous Agents and Multi-Agent Systems, 21(1):1–27, 2010.
  Details     BibTeX     Download: [pdf] (760.6kB )  [ps] (1.9MB )  

• A Task Specification Language for Bootstrap Learning.
  Ian Fasel, Michael Quinlan, and Peter Stone.
  In AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers, March 2009.
  AAAI Spring 2009 Symposium: Agents that Learn from Human Teachers
  Details     BibTeX     Download: [pdf] (407.2kB )  [ps] (1.9MB )  

• Generalized Model Learning for Reinforcement Learning in Factored Domains.
  Todd Hester and Peter Stone.
  In The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2009.
  AAMAS 2009
  Details     BibTeX     Download: [pdf] (181.5kB )  [ps] (425.5kB )  

• An Empirical Comparison of Abstraction in Models of Markov Decision Processes.
  Todd Hester and Peter Stone.
  In Proceedings of the ICML/UAI/COLT Workshop on Abstraction in Reinforcement Learning, June 2009.
  ICML ARL 2009
  Details     BibTeX     Download: [pdf] (127.0kB )  [ps] (363.0kB )  

• Feature Selection for Value Function Approximation Using Bayesian Model Selection.
  Tobias Jung and Peter Stone.
  In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2009.
  Details     BibTeX     Download: [pdf] (746.9kB )  [ps] (2.3MB )  [slides.pdf] (957.5kB )  

• An Empirical Analysis of Value Function-Based and Policy Search Reinforcement Learning.
  Shivaram Kalyanakrishnan and Peter Stone.
  In The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 749–756, International Foundation for Autonomous Agents and Multiagent Systems, May 2009.
  AAMAS 2009
  Details     BibTeX     Download: [pdf] (366.4kB )  [ps] (1.7MB )  

• Interactively Shaping Agents via Human Reinforcement: The TAMER Framework.
  W. Bradley Knox and Peter Stone.
  In The Fifth International Conference on Knowledge Capture, September 2009.
  The TAMER project page with videos of TAMER in action.
  K-CAP 2009
  Details     BibTeX     Download: [pdf] (540.2kB )  [ps] (3.7MB )  

• Design Principles for Creating Human-Shapable Agents.
  W. Bradley Knox, Ian Fasel, and Peter Stone.
  In AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers, March 2009.
  AAAI Spring 2009 Symposium: Agents that Learn from Human Teachers
  Details     BibTeX     Download: [pdf] (400.8kB )  [ps] (2.1MB )  

• Transfer Learning for Reinforcement Learning Domains: A Survey.
  Matthew E. Taylor and Peter Stone.
  Journal of Machine Learning Research, 10(1):1633–1685, 2009.
  Official version from journal website.
  Details     BibTeX     Download: [pdf] (399.8kB )  [ps] (579.4kB )  

• Instance-Based Action Models for Fast Action Planning.
  Mazda Ahmadi and Peter Stone.
  In Ubbo Visser, Fernando Ribeiro, Takeshi Ohashi, and Frank Dellaert, editors, RoboCup-2007: Robot Soccer World Cup XI, Lecture Notes in Artificial Intelligence, pp. 1–16, Springer Verlag, Berlin, 2008.
  BEST PAPER AWARD WINNER at RoboCup International Symposium.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (466.3kB )  [ps] (3.0MB )  

• Hierarchical Model-Based Reinforcement Learning: Rmax + MAXQ.
  Nicholas K. Jong and Peter Stone.
  In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008.
  ICML 2008
  Details     BibTeX     Download: [pdf] (157.5kB )  [ps] (370.2kB )  

• Model-based Reinforcement Learning in a Complex Domain.
  Shivaram Kalyanakrishnan, Peter Stone, and Yaxin Liu.
  In Ubbo Visser, Fernando Ribeiro, Takeshi Ohashi, and Frank Dellaert, editors, RoboCup-2007: Robot Soccer World Cup XI, Lecture Notes in Artificial Intelligence, pp. 171–83, Springer Verlag, Berlin, 2008.
  Details     BibTeX     Download: [pdf] (200.9kB )  [ps] (515.0kB )  

• TAMER: Training an Agent Manually via Evaluative Reinforcement.
  W. Bradley Knox and Peter Stone.
  In IEEE 7th International Conference on Development and Learning, August 2008.
  ICDL-2008
  Also available in IEEE Xplore, 9-12 Aug. 2008 Pages:292 - 297
  Details     BibTeX     Download: [pdf] (1.1MB )  [ps] (12.8MB )  

• Transferring Instances for Model-Based Reinforcement Learning.
  Matthew E. Taylor, Nicholas K. Jong, and Peter Stone.
  In Machine Learning and Knowledge Discovery in Databases, pp. 488–505, September 2008.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (304.9kB )  [ps] (860.1kB )  

• Autonomous Transfer for Reinforcement Learning.
  Matthew E. Taylor, Gregory Kuhlmann, and Peter Stone.
  In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.
  AAMAS-2008
  Details     BibTeX     Download: [pdf] (233.3kB )  [ps] (391.7kB )  

• IFSA: Incremental Feature-Set Augmentation for Reinforcement Learning Tasks.
  Mazda Ahmadi, Matthew E. Taylor, and Peter Stone.
  In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
  BEST PAPER AWARD NOMINEE.
  AAMAS-2007
  Details     BibTeX     Download: [pdf] (261.6kB )  [ps] (1.0MB )  

• The Chin Pinch: A Case Study in Skill Learning on a Legged Robot.
  Peggy Fidelman and Peter Stone.
  In Gerhard Lakemeyer, Elizabeth Sklar, Domenico Sorenti, and Tomoichi Takahashi, editors, RoboCup-2006: Robot Soccer World Cup X, Lecture Notes in Artificial Intelligence, pp. 59–71, Springer Verlag, Berlin, 2007.
  Some videos referenced in the paper.
  Details     BibTeX     Download: [pdf] (186.9kB )  [ps] (1.6MB )  

• Model-Based Function Approximation for Reinforcement Learning.
  Nicholas K. Jong and Peter Stone.
  In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
  Details     BibTeX     Download: [pdf] (321.1kB )  [ps] (1.0MB )  

• Model-Based Exploration in Continuous State Spaces.
  Nicholas K. Jong and Peter Stone.
  In The Seventh Symposium on Abstraction, Reformulation, and Approximation, July 2007.
  Details     BibTeX     Download: [pdf] (324.3kB )  [ps] (1.1MB )  

• Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study.
  Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone.
  In Gerhard Lakemeyer, Elizabeth Sklar, Domenico Sorenti, and Tomoichi Takahashi, editors, RoboCup-2006: Robot Soccer World Cup X, Lecture Notes in Artificial Intelligence, pp. 72–85, Springer Verlag, Berlin, 2007.
  BEST STUDENT PAPER AWARD WINNER at RoboCup International Symposium.
  Some simulations referenced in the paper.
  Details     BibTeX     Download: [pdf] (992.2kB )  [ps] (1.6MB )  

• Batch Reinforcement Learning in a Complex Domain.
  Shivaram Kalyanakrishnan and Peter Stone.
  In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 650–657, ACM, New York, NY, USA, May 2007.
  BEST PAPER AWARD NOMINEE.
  AAMAS-2007
  Details     BibTeX     Download: [pdf] (186.4kB )  [ps] (384.8kB )  

• Graph-Based Domain Mapping for Transfer Learning in General Games.
  Gregory Kuhlmann and Peter Stone.
  In Proceedings of The Eighteenth European Conference on Machine Learning, September 2007.
  Details     BibTeX     Download: [pdf] (377.0kB )  [ps] (601.6kB )  

• Autonomous Learning of Stable Quadruped Locomotion.
  Manish Saggar, Thomas D'Silva, Nate Kohl, and Peter Stone.
  In Gerhard Lakemeyer, Elizabeth Sklar, Domenico Sorenti, and Tomoichi Takahashi, editors, RoboCup-2006: Robot Soccer World Cup X, Lecture Notes in Artificial Intelligence, pp. 98–109, Springer Verlag, Berlin, 2007.
  BEST PAPER AWARD NOMINEE at RoboCup International Symposium.
  Some videos referenced in the paper.
  Details     BibTeX     Download: [pdf] (1.1MB )  [ps] (26.8MB )  

• Transfer Learning via Inter-Task Mappings for Temporal Difference Learning.
  Matthew E. Taylor, Peter Stone, and Yaxin Liu.
  Journal of Machine Learning Research, 8(1):2125–2167, 2007.
  Available from journal's web page.
  Details     BibTeX     Download: [pdf] (499.9kB )  [ps] (831.3kB )  

• Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison.
  Matthew E. Taylor, Shimon Whiteson, and Peter Stone.
  In Proceedings of the Twenty-Second Conference on Artificial Intelligence, pp. 1675–1678, July 2007. Nectar Track
  AAAI 2007
  Details     BibTeX     Download: [pdf] (99.7kB )  [ps] (190.4kB )  

• Cross-Domain Transfer for Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  In Proceedings of the Twenty-Fourth International Conference on Machine Learning, June 2007.
  ICML 2007
  Details     BibTeX     Download: [pdf] (220.7kB )  [ps] (325.4kB )  

• Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning.
  Matthew E. Taylor, Shimon Whiteson, and Peter Stone.
  In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
  AAMAS-2007
  Details     BibTeX     Download: [pdf] (222.5kB )  [ps] (525.2kB )  

• Representation Transfer for Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  In AAAI 2007 Fall Symposium on Computational Approaches to Representation Change during Learning and Development, November 2007.
  2007 AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development
  Details     BibTeX     Download: [pdf] (144.9kB )  [ps] (375.1kB )  

• Empirical Studies in Action Selection for Reinforcement Learning.
  Shimon Whiteson, Matthew E. Taylor, and Peter Stone.
  Adaptive Behavior, 15(1):33–50, March 2007.
  Details     BibTeX     Download: [pdf] (828.6kB )  [ps] (1.5MB )  

• Adaptive Tile Coding for Value Function Approximation.
  Shimon Whiteson, Matthew E. Taylor, and Peter Stone.
  Technical Report AI-TR-07-339, University of Texas at Austin, 2007.
  Details     BibTeX     Download: [pdf] (329.4kB )  [ps] (942.5kB )  

• Value Function Transfer for General Game Playing.
  Bikramjit Banerjee, Gregory Kuhlmann, and Peter Stone.
  In ICML workshop on Structural Knowledge Transfer for Machine Learning, June 2006.
  ICML 2006 workshop on Structural Knowledge Transfer for Machine Learning
  Details     BibTeX     Download: [pdf] (172.9kB )  [ps] (464.0kB )  

• Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping.
  Yaxin Liu and Peter Stone.
  In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 415–20, July 2006.
  AAAI 2006
  Details     BibTeX     Download: [pdf] (151.7kB )  [ps] (1.6MB )  

• Keepaway Soccer: From Machine Learning Testbed to Benchmark.
  Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, and Yaxin Liu.
  In Itsuki Noda, Adam Jacoff, Ansgar Bredenfeld, and Yasutake Takahashi, editors, RoboCup-2005: Robot Soccer World Cup IX, pp. 93–105, Springer Verlag, Berlin, 2006.
  Some simulations of keepaway referenced in the paper and keepaway software.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (567.7kB )  [ps] (2.3MB )  

• Comparing Evolutionary and Temporal Difference Methods for Reinforcement Learning.
  Matthew Taylor, Shimon Whiteson, and Peter Stone.
  In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1321–28, July 2006.
  BEST PAPER AWARD at GECCO 2006
  Details     BibTeX     Download: [pdf] (235.9kB )  [ps] (562.2kB )  

• Evolutionary Function Approximation for Reinforcement Learning.
  Shimon Whiteson and Peter Stone.
  Journal of Machine Learning Research, 7:877–917, May 2006.
  Available from journal's web page.
  Details     BibTeX     Download: [pdf] (1.7MB )  [ps] (6.1MB )  

• Function Approximation via Tile Coding: Automating Parameter Choice.
  Alexander A. Sherstov and Peter Stone.
  In J.-D. Zucker and I. Saitta, editors, SARA 2005, Lecture Notes in Artificial Intelligence, pp. 194–205, Springer Verlag, Berlin, 2005.
  SARA-05.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (187.7kB )  [ps] (583.4kB )  [slides.pdf] (193.7kB )  

• Reinforcement Learning for RoboCup-Soccer Keepaway.
  Peter Stone, Richard S. Sutton, and Gregory Kuhlmann.
  Adaptive Behavior, 13(3):165–188, 2005.
  Contains material that was previously published in an ICML-2001 paper and a RoboCup 2003 Symposium paper.
  Some simulations of keepaway referenced in the paper and keepaway software.
  Details     BibTeX     Download: [pdf] (1.2MB )  [ps] (2.0MB )  

• Value Functions for RL-Based Behavior Transfer: A Comparative Study.
  Matthew E. Taylor, Peter Stone, and Yaxin Liu.
  In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
  AAAI 2005
  Details     BibTeX     Download: [pdf] (147.3kB )  [ps] (449.9kB )  

• Behavior Transfer for Value-Function-Based Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 53–59, ACM Press, New York, NY, July 2005.
  AAMAS-2005
  Details     BibTeX     Download: [pdf] (230.4kB )  [ps] (620.1kB )  

• Machine Learning for Fast Quadrupedal Locomotion.
  Nate Kohl and Peter Stone.
  In The Nineteenth National Conference on Artificial Intelligence, pp. 611–616, July 2004.
  Some videos of walking robots referenced in the paper.
  AAAI 2004
  Details     BibTeX     Download: [pdf] (210.0kB )  [ps] (1.4MB )  

• Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion.
  Nate Kohl and Peter Stone.
  In Proceedings of the IEEE International Conference on Robotics and Automation, May 2004.
  Some videos of walking robots referenced in the paper.
  ICRA 2004
  Details     BibTeX     Download: [pdf] (301.5kB )  [ps] (3.2MB )  

• Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer.
  Gregory Kuhlmann, Peter Stone, Raymond Mooney, and Jude Shavlik.
  In The AAAI-2004 Workshop on Supervisory Control of Learning and Adaptive Systems, July 2004.
  Details     BibTeX     Download: [pdf] (170.3kB )  [ps] (498.2kB )  

• Adaptive Job Routing and Scheduling.
  Shimon Whiteson and Peter Stone.
  Engineering Applications of Artificial Intelligence, 17(7):855–69, October 2004. Special issue on Autonomic Computing and Automation
  Available from the publisher's webpage
  The version from this page corrects a minor error in the published version.
  An earlier version appeared in the proceedings of The Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI 2004)
  Details     BibTeX     Download: [pdf] (664.7kB )  [ps] (1.1MB )  

• TPOT-RL Applied to Network Routing.
  Peter Stone.
  In Proceedings of the Seventeenth International Conference on Machine Learning, pp. 935–942, 2000.
  ICML-2000
  Details     BibTeX     Download: [pdf] (161.7kB )  [ps] (395.0kB )  

• Team-Partitioned, Opaque-Transition Reinforcement Learning.
  Peter Stone and Manuela Veloso.
  In Minoru Asada and Hiroaki Kitano, editors, RoboCup-98: Robot Soccer World Cup II, Lecture Notes in Artificial Intelligence, pp. 261–72, Springer Verlag, Berlin, 1999. Also in Proceedings of the Third International Conference on Autonomous Agents, 1999
  Details     BibTeX     Download: [pdf] (151.4kB )  [ps] (144.7kB )  

Machine Learning: Transfer Learning

• Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning.
  Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, and Roberto Martin-Martin.
  Reinforcement Learning Journal, 7, August 2026.
  Official version
  Details     BibTeX     Download: [pdf] (7.9MB )  

• FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning.
  Jiaheng Hu, Rose Hendrix, Ali Farhadi, Aniruddha Kembhavi, Roberto Martín-Martín, Peter Stone, Kuo-Hao Zeng, and Kiana Ehsani.
  In ICRA, May 2025.
  Details     BibTeX     Download: [pdf] (9.2MB )  

• Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference-Aligned Path Planning.
  Haresh, Karnan; Elvin, Yang; Garrett, Warnell; Joydeep, Biswas; Peter, and Stone.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (4.3MB )  

• Task Phasing: Automated Curriculum Learning from Demonstrations.
  Vaibhav Bajaj, Guni Sharon, and Peter Stone.
  In Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS 2023), July 2023.
  Accompanying code
  Details     BibTeX     Download: [pdf] (416.0kB )  

• STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience.
  Haresh Karnan, Elvin Yang, Daniel Farkash, Garrett Warnell, Joydeep Biswas, and Peter Stone.
  In The Conference on Robot Learning (CoRL), November 2023.
  Poster, Video, Project Website
  Details     BibTeX     Download: [pdf] (25.7MB )  

• Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis.
  Zifan Xu, Anirudh Nair, Xuesu Xiao, and Peter Stone.
  In IROS Workshop on Photorealistic Image and Environment Synthesis for Robotics (PIES-Rob) , January 2023.
  Details     BibTeX     Download: [pdf] (4.3MB )  

• VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics.
  Haresh Karnan, Kavan Sikand, Pranav Atreya, Sadegh Rabiee, Xuesu Xiao, Garrett Warnell, Peter Stone, and Joydeep Biswas.
  In International Conference on Intelligent Robots and Systems, 2022, October 2022.
  Details     BibTeX     Download: [pdf] (3.0MB )  

• VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation.
  Haresh Karnan, Garrett Warnell, Xuesu Xiao, and Peter Stone.
  In International Conference on Robotics and Automation, 2022, May 2022.
  Poster, Video
  Details     BibTeX     Download: [pdf] (1.5MB )  

• Adversarial Imitation Learning from Video using a State Observer.
  Haresh Karnan, Garrett Warnell, Faraz Torabi, and Peter Stone.
  In International Conference on Robotics and Automation, 2022, May 2022.
  Video
  Details     BibTeX     Download: [pdf] (933.2kB )  

• Grounded Action Transformation for Sim-to-Real Reinforcement Learning.
  Josiah P. Hanna, Siddharth Desai, Haresh Karnan, Garrett Warnell, and Peter Stone.
  Special Issue on Reinforcement Learning for Real Life, Machine Learning, 2021, May 2021.
  Details     BibTeX     Download: [pdf] (3.0MB )  

• An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch.
  Siddarth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, and Peter Stone.
  In Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS 2020), December 2020.
  Poster
  Details     BibTeX     Download: [pdf] (1.3MB )  

• Stochastic Grounded Action Transformation for Robot Learning in Simulation.
  Siddharth Desai, Haresh Karnan, Josiah P. Hanna, Garrett Warnell, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020), October 2020.
  11-minute video presentation.
  Details     BibTeX     Download: [pdf] (1.9MB )  

• Reinforced Grounded Action Transformation for Sim-to-Real Transfer.
  Haresh Karnan, Siddharth Desai, Josiah P. Hanna, Garrett Warnell, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020), October 2020.
  14-minute video presentation.
  Details     BibTeX     Download: [pdf] (506.6kB )  

• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey.
  Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, and Peter Stone.
  Journal of Machine Learning Research, 21(181):1–50, 2020.
  Details     BibTeX     Download: [pdf] (1.4MB )  

• Generalizing Curricula for Reinforcement Learning.
  Sanmit Narvekar and Peter Stone.
  In 4th Lifelong Learning Workshop at the International Conference on Machine Learning (ICML 2020), July 2020.
  Details     BibTeX     Download: [pdf] (330.4kB )  [slides.pdf] (3.8MB )  

• Learning Curriculum Policies for Reinforcement Learning.
  Sanmit Narvekar and Peter Stone.
  In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2019.
  Details     BibTeX     Download: [pdf] (953.0kB )  [slides.pdf] (5.6MB )  

• Agents teaching agents: a survey on inter-agent transfer learning.
  Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone.
  Autonomous Agents and Multi-Agent Systems, Dec 2019.
  Official version from JAAMAS
  Details     BibTeX     Download: [pdf] (572.4kB )  

• Building Self-Play Curricula Online by Playing with Expert Agents in Adversarial Games.
  Felipe Leno Da Silva, Anna Helena Reali Costa, and Peter Stone.
  In Proceedings of the 8th Brazilian Conference on Intelligent Systems (BRACIS), October 2019.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning.
  Sanmit Narvekar, Jivko Sinapov, and Peter Stone.
  In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), August 2017.
  Details     BibTeX     Download: [pdf] (826.2kB )  [slides.pdf] (5.8MB )  

• Automatic Curriculum Graph Generation for Reinforcement Learning Agents.
  Maxwell Svetlik, Matteo Leonetti, Jivko Sinapov, Rishi Shah, Nick Walker, and Peter Stone.
  In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), February 2017.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• Making Friends on the Fly: Cooperating with New Teammates.
  Samuel Barrett, Avi Rosenfeld, Sarit Kraus, and Peter Stone.
  Artificial Intelligence, October 2016.
  Official version from journal website.
  Details     BibTeX     Download: [pdf] (917.9kB )  

• Source Task Creation for Curriculum Learning.
  Sanmit Narvekar, Jivko Sinapov, Matteo Leonetti, and Peter Stone.
  In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), May 2016.
  Details     BibTeX     Download: [pdf] (630.0kB )  [slides.pdf] (10.2MB )  

• Learning Inter-Task Transferability in the Absence of Target Task Samples.
  Jivko Sinapov, Sanmit Narvekar, Matteo Leonetti, and Peter Stone.
  In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015.
  Details     BibTeX     Download: [pdf] (337.2kB )  

• An Introduction to Inter-task Transfer for Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  AI Magazine, 32(1):15–34, 2011.
  Details     BibTeX     Download: [pdf] (237.0kB )  [ps] (773.0kB )  

• Transfer Learning for Reinforcement Learning on a Physical Robot.
  Samuel Barrett, Matt E. Taylor, and Peter Stone.
  In Ninth International Conference on Autonomous Agents and Multiagent Systems - Adaptive Learning Agents Workshop (AAMAS - ALA), May 2010.
  AAMAS ALA 2010
  Details     BibTeX     Download: [pdf] (688.8kB )  [ps] (5.7MB )  

• Boosting for Regression Transfer.
  David Pardoe and Peter Stone.
  In Proceedings of the 27th International Conference on Machine Learning (ICML), June 2010.
  Some of the data used in the experiments.
  Details     BibTeX     Download: [pdf] (600.5kB )  [ps] (1.7MB )  

• Transfer Learning for Reinforcement Learning Domains: A Survey.
  Matthew E. Taylor and Peter Stone.
  Journal of Machine Learning Research, 10(1):1633–1685, 2009.
  Official version from journal website.
  Details     BibTeX     Download: [pdf] (399.8kB )  [ps] (579.4kB )  

• Transferring Instances for Model-Based Reinforcement Learning.
  Matthew E. Taylor, Nicholas K. Jong, and Peter Stone.
  In Machine Learning and Knowledge Discovery in Databases, pp. 488–505, September 2008.
  Official version from Publisher's Webpage© Springer-Verlag
  Details     BibTeX     Download: [pdf] (304.9kB )  [ps] (860.1kB )  

• Autonomous Transfer for Reinforcement Learning.
  Matthew E. Taylor, Gregory Kuhlmann, and Peter Stone.
  In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.
  AAMAS-2008
  Details     BibTeX     Download: [pdf] (233.3kB )  [ps] (391.7kB )  

• Transfer Learning and Intelligence: an Argument and Approach.
  Matthew E. Taylor, Gregory Kuhlmann, and Peter Stone.
  In Proceedings of the First Conference on Artificial General Intelligence, March 2008.
  AGI-2008
  Google video version of the conference presentation.
  Details     BibTeX     Download: [pdf] (149.0kB )  [ps] (202.5kB )  

• General Game Learning using Knowledge Transfer.
  Bikramjit Banerjee and Peter Stone.
  In The 20th International Joint Conference on Artificial Intelligence, pp. 672–677, January 2007.
  IJCAI-07
  Details     BibTeX     Download: [pdf] (118.1kB )  [ps] (198.5kB )  

• Graph-Based Domain Mapping for Transfer Learning in General Games.
  Gregory Kuhlmann and Peter Stone.
  In Proceedings of The Eighteenth European Conference on Machine Learning, September 2007.
  Details     BibTeX     Download: [pdf] (377.0kB )  [ps] (601.6kB )  

• Transfer Learning via Inter-Task Mappings for Temporal Difference Learning.
  Matthew E. Taylor, Peter Stone, and Yaxin Liu.
  Journal of Machine Learning Research, 8(1):2125–2167, 2007.
  Available from journal's web page.
  Details     BibTeX     Download: [pdf] (499.9kB )  [ps] (831.3kB )  

• Cross-Domain Transfer for Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  In Proceedings of the Twenty-Fourth International Conference on Machine Learning, June 2007.
  ICML 2007
  Details     BibTeX     Download: [pdf] (220.7kB )  [ps] (325.4kB )  

• Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning.
  Matthew E. Taylor, Shimon Whiteson, and Peter Stone.
  In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
  AAMAS-2007
  Details     BibTeX     Download: [pdf] (222.5kB )  [ps] (525.2kB )  

• Representation Transfer for Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  In AAAI 2007 Fall Symposium on Computational Approaches to Representation Change during Learning and Development, November 2007.
  2007 AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development
  Details     BibTeX     Download: [pdf] (144.9kB )  [ps] (375.1kB )  

• Accelerating Search with Transferred Heuristics.
  Matthew E. Taylor, Gregory Kuhlmann, and Peter Stone.
  In ICAPS-07 workshop on AI Planning and Learning, September 2007.
  ICAPS 2007 workshop on AI Planning and Learning
  Details     BibTeX     Download: [pdf] (139.9kB )  [ps] (215.4kB )  

• Value Function Transfer for General Game Playing.
  Bikramjit Banerjee, Gregory Kuhlmann, and Peter Stone.
  In ICML workshop on Structural Knowledge Transfer for Machine Learning, June 2006.
  ICML 2006 workshop on Structural Knowledge Transfer for Machine Learning
  Details     BibTeX     Download: [pdf] (172.9kB )  [ps] (464.0kB )  

• Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping.
  Yaxin Liu and Peter Stone.
  In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 415–20, July 2006.
  AAAI 2006
  Details     BibTeX     Download: [pdf] (151.7kB )  [ps] (1.6MB )  

• State Abstraction Discovery from Irrelevant State Variables.
  Nicholas K. Jong and Peter Stone.
  In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, pp. 752–757, August 2005.
  Details     BibTeX     Download: [pdf] (102.1kB )  [ps] (249.8kB )  [slides.pdf] (323.4kB )  

• Improving Action Selection in MDP's via Knowledge Transfer.
  Alexander A. Sherstov and Peter Stone.
  In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
  AAAI 2005
  Details     BibTeX     Download: [pdf] (205.5kB )  [ps] (774.2kB )  

• Value Functions for RL-Based Behavior Transfer: A Comparative Study.
  Matthew E. Taylor, Peter Stone, and Yaxin Liu.
  In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
  AAAI 2005
  Details     BibTeX     Download: [pdf] (147.3kB )  [ps] (449.9kB )  

• Behavior Transfer for Value-Function-Based Reinforcement Learning.
  Matthew E. Taylor and Peter Stone.
  In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 53–59, ACM Press, New York, NY, July 2005.
  AAMAS-2005
  Details     BibTeX     Download: [pdf] (230.4kB )  [ps] (620.1kB )  

Machine Learning: Imitation Learning

• RoboSSM: Scalable In-Context Imitation Learning Via State-Space Models.
  Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Martín-Martín, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2026.
  Details     BibTeX     Download: [pdf] (2.8MB )  

• Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds.
  Rohan Chandra, Haresh Karnan, Negar Mehr, Peter Stone, and Joydeep Biswas.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2025.
  Details     BibTeX     Download: [pdf] (695.7kB )  

• Telemoma: A Modular and Versatile Teleoperation System for Mobile Manipulation.
  Shivin Dass, Wensi Ai, Yuqian Jiang, Samik Singh, Jiaheng Hu, Ruohan Zhang, Peter Stone, Ben Abbatematteo, and Roberto Martin-Martin.
  In ICRA Workshop on Mobile Manipulation and Embodied Intelligence, May 2024.
  Details     BibTeX     Download: [pdf] (2.9MB )  

• Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds.
  Amir Hossain Raj, Zichao Hu, Haresh Karnan, Rohan Chandra, Amirreza Payandeh, Luisa Mao, Peter Stone, Joydeep Biswas, and and Xuesu Xiao.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making.
  William Yue, Bo Liu, and Peter Stone.
  In Conference on Lifelong Learning Agents (CoLLAs), July 2024.
  Details     BibTeX     Download: [pdf] (599.7kB )  [poster.pdf] (709.1kB )  

• STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience.
  Haresh Karnan, Elvin Yang, Daniel Farkash, Garrett Warnell, Joydeep Biswas, and Peter Stone.
  In The Conference on Robot Learning (CoRL), November 2023.
  Poster, Video, Project Website
  Details     BibTeX     Download: [pdf] (25.7MB )  

• LIBERO: Benchmarking Knowledge Transfer in Lifelong Robot Learning.
  Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone.
  In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks, December 2023.
  Details     BibTeX     Download: [pdf] (37.6MB )  [poster.pdf] (2.6MB )  

• D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning.
  Caroline Wang, Garrett Warnell, and Peter Stone.
  In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2023.
  Details     BibTeX     Download: [pdf] (1.6MB )  [slides.pdf] (2.4MB )  [poster.pdf] (1.4MB )  

• DM$^2$: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching.
  Caroline Wang, Ishan Durugkar, Elad Liebman, and Peter Stone.
  In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), February 2023.
  Details     BibTeX     Download: [pdf] (801.2kB )  [slides.pdf] (3.7MB )  [poster.pdf] (1.4MB )  

• Learning Generalizable Manipulation Policies with Object-Centric 3D Representations.
  Yifeng Zhu, Zhenyu Jiang, Peter Stone, and Yuke Zhu.
  In Conference on Robot Learning (CoRL), November 2023.
  Details     BibTeX     Download: [pdf] (7.3MB )  [poster.pdf] (5.2MB )  

• Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch.
  Eddy Hudson, Garrett Warnell, Faraz Torabi, and Peter Stone.
  In International Conference on Robotics and Automation (ICRA), May 2022.
  Presentation Video
  Details     BibTeX     Download: [pdf] (2.5MB )  

• Socially CompliAnt Navigation Dataset (SCAND): A Large-Scale Dataset Of Demonstrations For Social Navigation.
  Haresh Karnan, Anirudh Nair, Xuesu Xiao, Garrett Warnell, Soren Pirk, Alexander Toshev, Justin Hart, Joydeep Biswas, and Peter Stone.
  Robotics and Automation Letters (RA-L), 2022, 7:11807–14, October 2022.
  Dataset; Poster; Video Presentation
  Details     BibTeX     Download: [pdf] (4.2MB )  

• VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics.
  Haresh Karnan, Kavan Sikand, Pranav Atreya, Sadegh Rabiee, Xuesu Xiao, Garrett Warnell, Peter Stone, and Joydeep Biswas.
  In International Conference on Intelligent Robots and Systems, 2022, October 2022.
  Details     BibTeX     Download: [pdf] (3.0MB )  

• VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation.
  Haresh Karnan, Garrett Warnell, Xuesu Xiao, and Peter Stone.
  In International Conference on Robotics and Automation, 2022, May 2022.
  Poster, Video
  Details     BibTeX     Download: [pdf] (1.5MB )  

• Adversarial Imitation Learning from Video using a State Observer.
  Haresh Karnan, Garrett Warnell, Faraz Torabi, and Peter Stone.
  In International Conference on Robotics and Automation, 2022, May 2022.
  Video
  Details     BibTeX     Download: [pdf] (933.2kB )  

• Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation.
  Yifeng Zhu, Peter Stone, and Yuke Zhu.
  IEEE Robotics and Automation Letters (RA-L), 7:4126–33, April 2022.
  Project page
  Code
  Details     BibTeX     Download: [pdf] (9.3MB )  

• VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors.
  Yifeng Zhu, Abhishek Joshi, Peter Stone, and Yuke Zhu.
  In Proceedings of the 6th Conference on Robot Learning (CoRL 2022), December 2022.
  Project page
  Code
  Details     BibTeX     Download: [pdf] (4.4MB )  

• RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning.
  Eddy Hudson, Garrett Warnell, and Peter Stone.
  In Autonomous Robots and Multirobot Systems Workshop at the 20th International Conference onAutonomous Agents and Multiagent Systems (AAMAS 2021), May 2021.
  Details     BibTeX     Download: [pdf] (393.0kB )  

• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation.
  Faraz Torabi, Garrett Warnell, and Peter Stone.
  In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (742.7kB )  

• APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback.
  Zizhao Wang, Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), October 2021.
  5-minute Video Presentation; 15-minute Video Presentation
  Details     BibTeX     Download: [pdf] (1.1MB )  [slides.pdf] (2.7MB )  

• From Agile Ground to Aerial Navigation: Learning from Learned Hallucination.
  Zizhao Wang, Xuesu Xiao, Alexander J Nettekoven, Kadhiravan Umasankar, Anika Singh, Sriram Bommakanti, Ufuk Topcu, and Peter Stone.
  In Proceedings of the International Conference on Intelligent Robots and Systems (IROS 2021), October 2021.
  1-minute Video Summary; 15-minute Video Presentation
  Details     BibTeX     Download: [pdf] (2.9MB )  [slides.pdf] (2.7MB )  

• APPLI: Adaptive Planner Parameter Learning From Interventions.
  Zizhao Wang, Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone.
  In Proceedings of the International Conference on Robotics and Automation (ICRA 2021), May 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (4.2MB )  

• Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain.
  Xuesu Xiao, Joydeep Biswas, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), July 2021.
  Contains material that was previously presented in an ICRA21 workshop paper Video
  Details     BibTeX     Download: [pdf] (3.2MB )  [slides.pdf] (23.2MB )  

• Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination.
  Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), January 2021.
  5-minute video demonstration
  Project webpage
  Details     BibTeX     Download: [pdf] (3.2MB )  

• Agile Robot Navigation through Hallucinated Learning and Sober Deployment.
  Xuesu Xiao, Bo Liu, and Peter Stone.
  In Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021), June 2021.
  Video
  Details     BibTeX     Download: [pdf] (3.1MB )  

• RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration.
  Brahma Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), 5:6262–69, October 2020.
  Video of the experiments; 13-minute video presentation.
  Details     BibTeX     Download: [pdf] (405.1kB )  [slides.pptx] (115.4MB )  

• APPLD: Adaptive Planner Parameter Learning from Demonstration.
  Xuesu Xiao, Bo Liu, Garrett Warnell, Jonathan Fink, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), June 2020.
  Presented at International Conference on Intelligent Robots and Systems ({IROS})\\ 5-minute Video presentation; 15-minute Video presentation
  Project webpage
  Details     BibTeX     Download: [pdf] (2.2MB )  [slides.pdf] (21.1MB )  

• Agents teaching agents: a survey on inter-agent transfer learning.
  Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone.
  Autonomous Agents and Multi-Agent Systems, Dec 2019.
  Official version from JAAMAS
  Details     BibTeX     Download: [pdf] (572.4kB )  

• Imitation Learning from Video by Leveraging Proprioception.
  Faraz Torabi, Garrett Warnell, and Peter Stone.
  In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), August 2019.
  Details     BibTeX     Download: [pdf] (1.1MB )  [slides.pptx] (20.3MB )  

• Recent Advances in Imitation Learning from Observation.
  Faraz Torabi, Garrett Warnell, and Peter Stone.
  In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), August 2019.
  Details     BibTeX     Download: [pdf] (157.4kB )  [slides.pptx] (45.5MB )  

• Generative Adversarial Imitation from Observation.
  Faraz Torabi, Garrett Warnell, and Peter Stone.
  In Imitation, Intent, and Interaction (I3) Workshop at ICML 2019, June 2019.
  Details     BibTeX     Download: [pdf] (1.0MB )  

• Sample-efficient Adversarial Imitation Learning from Observation.
  Faraz Torabi, Sean Geiger, Garrett Warnell, and Peter Stone.
  In Imitation, Intent, and Interaction (I3) Workshop at ICML 2019, June 2019.
  Details     BibTeX     Download: [pdf] (6.1MB )  

• Behavioral Cloning from Observation.
  Faraz Torabi, Garrett Warnell, and Peter Stone.
  In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), July 2018.
  Also available from arXiv
  Details     BibTeX     Download: [pdf] (2.3MB )  [slides.pptx] (26.0MB )  

Machine Learning: Learning from Humans

• Influencing Humans to Conform to Preference Models for RLHF.
  Stephane Hatgis-Kessell, W. Bradley Knox, Serena Booth, and Peter Stone.
  Transactions of Machine Learning (TMLR), August 2026.
  Details     BibTeX     Download: [pdf] (17.4MB )  

• Coadaptive Value Alignment.
  Nathan Tsoi, Eric Hsiung, Masayuki Yamazaki, Swarat Chaudhuri, Peter Stone, and and Joydeep Biswas.
  In Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems, January 2026.
  Details     BibTeX     Download: [pdf] (637.6kB )  

• RoboSSM: Scalable In-Context Imitation Learning Via State-Space Models.
  Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Martín-Martín, and Peter Stone.
  In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2026.
  Details     BibTeX     Download: [pdf] (2.8MB )  

• MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention.
  Yuxin Chen, Chen Tang, Jianglan Wei, Chenran Li, Ran Tian, Xiang Zhang, Wei Zhan, Peter Stone, and Masayoshi Tomizuka.
  In Conference on Robot Learning (CoRL), September 2025.
  Details     BibTeX     Download: [pdf] (18.9MB )  

• Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference-Aligned Path Planning.
  Haresh, Karnan; Elvin, Yang; Garrett, Warnell; Joydeep, Biswas; Peter, and Stone.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (4.3MB )  

• Learning Optimal Advantage from Preferences and Mistaking it for Reward.
  W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson, Serena Booth, Anca Dragan, Peter Stone, and Scott Niekum.
  In The 38th Annual AAAI Conference on Artificial Intelligence (AAAI), February 2024.
  Details     BibTeX     Download: [pdf] (3.6MB )  [slides.pdf] (3.9MB )  [poster.pdf] (2.9MB )  

• Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds.
  Amir Hossain Raj, Zichao Hu, Haresh Karnan, Rohan Chandra, Amirreza Payandeh, Luisa Mao, Peter Stone, Joydeep Biswas, and and Xuesu Xiao.
  In International Conference on Robotics and Automation, May 2024.
  Details     BibTeX     Download: [pdf] (2.0MB )  

• The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications.
  Serena Booth, W Bradley Knox, Julie Shah, Scott Niekum, Peter Stone, and Alessandro Allievi.
  In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), Feb 2023.
  Project page with slides and video.
  Details     BibTeX     Download: [pdf] (1.4MB )  

• Models of human preference for learning reward functions.
  W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, and Alessandro Allievi.
  Transactions on Machine Learning Research (TMLR), 2023.
  Details     BibTeX     Download: [pdf] (6.7MB )  [slides.pdf] (13.4MB )  

• Reward (Mis)design for Autonomous Driving.
  W. Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone.
  Artificial Intelligence, 316:103829, 2023.
  Paper webpage
  Details     BibTeX     Download: [pdf] (696.3kB )  [ps] (5.6MB )  

• Visually Adaptive Geometric Navigation.
  Shravan Ravi, Gary Wang, Shreyas Satewar, Xuesu Xiao, Garrett Warnell, Joydeep Biswas, and Peter Stone.
  In IEEE International Symposium on Safety,Security,and Rescue Robotics, November 2023.
  Details     BibTeX     Download: [pdf] (4.8MB )  

• Socially CompliAnt Navigation Dataset (SCAND): A Large-Scale Dataset Of Demonstrations For Social Navigation.
  Haresh Karnan, Anirudh Nair, Xuesu Xiao, Garrett Warnell, Soren Pirk, Alexander Toshev, Justin Hart, Joydeep Biswas, and Peter Stone.
  Robotics and Automation Letters (RA-L), 2022, 7:11807–14, October 2022.
  Dataset; Poster; Video Presentation
  Details     BibTeX     Download: [pdf] (4.2MB )  

• APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback.
  Zizhao Wang, Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), October 2021.
  5-minute Video Presentation; 15-minute Video Presentation
  Details     BibTeX     Download: [pdf] (1.1MB )  [slides.pdf] (2.7MB )  

• From Agile Ground to Aerial Navigation: Learning from Learned Hallucination.
  Zizhao Wang, Xuesu Xiao, Alexander J Nettekoven, Kadhiravan Umasankar, Anika Singh, Sriram Bommakanti, Ufuk Topcu, and Peter Stone.
  In Proceedings of the International Conference on Intelligent Robots and Systems (IROS 2021), October 2021.
  1-minute Video Summary; 15-minute Video Presentation
  Details     BibTeX     Download: [pdf] (2.9MB )  [slides.pdf] (2.7MB )  

• APPLI: Adaptive Planner Parameter Learning From Interventions.
  Zizhao Wang, Xuesu Xiao, Bo Liu, Garrett Warnell, and Peter Stone.
  In Proceedings of the International Conference on Robotics and Automation (ICRA 2021), May 2021.
  Video presentation
  Details     BibTeX     Download: [pdf] (4.2MB )  

• Recent Advances in Leveraging Human Guidance for Sequential Decision-Making Tasks.
  Ruohan Zhang, Faraz Torabi, Garrett Warnell, and Peter Stone.
  Autonomous Agents and Multi-Agent Systems, 35(31), June 2021.
  official online version
  Details     BibTeX     Download: [pdf] (3.8MB )  

• The EMPATHIC Framework for Task Learning from Implicit Human Feedback.
  Yuchen Cui, Qiping Zhang, Alessandro Allievi, Peter Stone, Scott Niekum, and W. Bradley Knox.
  In Proceedings of the 4th Conference on Robot Learning (CoRL 2020), November 2020.
  5-minute video presentation; 47-minute in-depth talk.
  Project website.
  Raw data from experiments.
  Details     BibTeX     Download: [pdf] (6.4MB )  [slides.pptx] (59.4MB )  

• Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog.
  Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney.
  The Journal of Artificial Intelligence Research (JAIR), 67, February 2020.
  Details     BibTeX     Download: [pdf] (4.0MB )  

• APPLD: Adaptive Planner Parameter Learning from Demonstration.
  Xuesu Xiao, Bo Liu, Garrett Warnell, Jonathan Fink, and Peter Stone.
  IEEE Robotics and Automation Letters (RA-L), June 2020.
  Presented at International Conference on Intelligent Robots and Systems ({IROS})\\ 5-minute Video presentation; 15-minute Video presentation
  Project webpage
  Details     BibTeX     Download: [pdf] (2.2MB )  [slides.pdf] (21.1MB )  

• Agents teaching agents: a survey on inter-agent transfer learning.
  Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone.
  Autonomous Agents and Multi-Agent Systems, Dec 2019.
  Official version from JAAMAS
  Details     BibTeX     Download: [pdf] (572.4kB )  

• Improving Grounded Natural Language Understanding through Human-Robot Dialog.
  Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond Mooney.
  In Proceedings of the International Conference on Robotics and Automation (ICRA 2019), May 2019.
  Details     BibTeX     Download: [pdf] (1.6MB )  

• Deep TAMER: Interactive agent shaping in high-dimensional state spaces.
  Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, and Peter Stone.
  In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, February 2018.
  Details     BibTeX     Download: [pdf] (1.6MB )  [slides.pptx] (16.2MB )  

• Framing reinforcement learning from human reward: Reward positivity, temporal discounting, episodicity, and performance.
  W. Bradley Knox and Peter Stone.
  Artificial Intelligence, 225(), August 2015.
  Artificial Intelligence
  Contains material that was previously published in a IUI 2013 paper and a RoMan 2012 paper that was nominated as a CoTeSys Cognitive Robotics BEST PAPER AWARD FINALIST.
  Details     BibTeX     Download: [pdf] (3.9MB )  

• Training a Robot via Human Feedback: A Case Study.
  W. Bradley Knox, Peter Stone, and Cynthia Breazeal.
  In International Conference on Social Robotics, October 2013.
  BEST PAPER AWARD WINNER at ICSR 2013
  An associated video summarizing the paper (direct link).
  Details     BibTeX     Download: [pdf] (2.3MB )  

• How Humans Teach Agents: A New Experimental Perspective.
  W. Bradley Knox, Brian D. Glass, Bradley C. Love, W. Todd Maddox, and Peter Stone.
  International Journal of Social Robotics, 4:409–421, Springer Netherlands, October 2012. 10.1007/s12369-012-0163-x
  International Journal of Social Robotics
  Download article from publisher
  Details     BibTeX     Download: [pdf] (2.6MB )  

• The Nature of Belief-Directed Exploratory Choice in Human Decision-Making.
  W. Bradley Knox, A. Ross Otto, Peter Stone, and Bradley Love.
  Frontiers in Psychology, 2(398), January 2012.
  Frontiers in Psychology
  Download article from publisher (free)
  A follow-up commentary by Erica Yu.
  Details     BibTeX     Download: (unavailable)

• Reinforcement Learning from Simultaneous Human and MDP Reward.
  W. Bradley Knox and Peter Stone.
  In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), June 2012.
  AAMAS 2012
  Details     BibTeX     Download: [pdf] (929.6kB )  [ps] (4.9MB )  

• Learning from feedback on actions past and intended.
  W. Bradley Knox, Cynthia Breazeal, and Peter Stone.
  In Proceedings of 7th ACM/IEEE International Conference on Human-Robot Interaction, Late-Breaking Reports Session (HRI), March 2012.
  HRI 2012
  Details     BibTeX     Download: [pdf] (447.6kB )  [ps] (3.9MB )  

• Understanding Human Teaching Modalities in Reinforcement Learning Environments: A Preliminary Report.
  W. Bradley Knox and Peter Stone.
  In IJCAI 2011 Workshop on Agents Learning Interactively from Human Teachers (ALIHT), July 2011.
  IJCAI 2011 Workshop on Agents Learning Interactively from Human Teachers (ALIHT)
  Details     BibTeX     Download: [pdf] (372.4kB )  [ps] (41.6MB )  

• Combining Manual Feedback with Subsequent MDP Reward Signals for Reinforcement Learning.
  W. Bradley Knox and Peter Stone.
  In Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), May 2010.
  Winner of the Pragnesh Jay Modi BEST STUDENT PAPER AWARD (and best paper award nominee).
  The TAMER project page with videos of TAMER in action.
  AAMAS-2010
  Details     BibTeX     Download: [pdf] (422.9kB )  [ps] (3.5MB )  

• Interactively Shaping Agents via Human Reinforcement: The TAMER Framework.
  W. Bradley Knox and Peter Stone.
  In The Fifth International Conference on Knowledge Capture, September 2009.
  The TAMER project page with videos of TAMER in action.
  K-CAP 2009
  Details     BibTeX     Download: [pdf] (540.2kB )  [ps] (3.7MB )  

• Design Principles for Creating Human-Shapable Agents.
  W. Bradley Knox, Ian Fasel, and Peter Stone.
  In AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers, March 2009.
  AAAI Spring 2009 Symposium: Agents that Learn from Human Teachers
  Details     BibTeX     Download: [pdf] (400.8kB )  [ps] (2.1MB )  

• TAMER: Training an Agent Manually via Evaluative Reinforcement.
  W. Bradley Knox and Peter Stone.
  In IEEE 7th International Conference on Development and Learning, August 2008.
  ICDL-2008
  Also available in IEEE Xplore, 9-12 Aug. 2008 Pages:292 - 297
  Details     BibTeX     Download: [pdf] (1.1MB )  [ps] (12.8MB )  

Machine Learning: Lifelong Learning

• Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning.
  Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, and Roberto Martin-Martin.
  Reinforcement Learning Journal, 7, August 2026.
  Official version
  Details     BibTeX     Download: [pdf] (7.9MB )  

• FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning.
  Jiaheng Hu, Rose Hendrix, Ali Farhadi, Aniruddha Kembhavi, Roberto Martín-Martín, Peter Stone, Kuo-Hao Zeng, and Kiana Ehsani.
  In ICRA, May 2025.
  Details     BibTeX     Download: [pdf] (9.2MB )  

• Longhorn: State Space Models are Amortized Online Learners.
  Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, and Qiang Liu.
  In International Conference on Learning Representations, April 2025.
  Details     BibTeX     Download: [pdf] (877.2kB )  

• t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making.
  William Yue, Bo Liu, and Peter Stone.
  In Conference on Lifelong Learning Agents (CoLLAs), July 2024.
  Details     BibTeX     Download: [pdf] (599.7kB )  [poster.pdf] (709.1kB )  

• A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems.
  Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, and Gautam K. Vallabha.
  Neural Networks, pp. 274–96, March 2023.
  Available from https://arxiv.org/abs/2301.07799
  Official version on publisher's website
  Details     BibTeX     Download: (unavailable)

• LIBERO: Benchmarking Knowledge Transfer in Lifelong Robot Learning.
  Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone.
  In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks, December 2023.
  Details     BibTeX     Download: [pdf] (37.6MB )  [poster.pdf] (2.6MB )  

• Continual Learning and Private Unlearning.
  Bo Liu, Qiang Liu, and Peter Stone.
  In Proceedings of the 1st Conference on Lifelong Learning Agents (CoLLA), August 2022.
  Details     BibTeX     Download: [pdf] (440.4kB )  [slides.pdf] (710.5kB )  

• A Rule-based Shield: Accumulating Safety Rules from Catastrophic Action Effects.
  Shahaf Shperberg, Bo Liu, Allessandro Allievi, and Peter Stone.
  In Proceedings of the 1st Conference on Lifelong Learning Agents (CoLLA), August 2022.
  Details     BibTeX     Download: [pdf] (8.9MB )