Reinforcement Learning
Reinforcement Learning tasks are learning problems where the desired behavior is not known; only sparse feedback on how well the agent is doing is provided. Reinforcement Learning techniques include value-function and policy iteration methods (note that although evolutionary computation and neuroevolution can also be seen as reinforcement learning methods, they are presented separately in this area hierarchy).
Subareas:
People
     [Expand to show all 26][Minimize]
Adrian Agogino Formerly affiliated Collaborator adrian k agogino [at] nasa gov
Samuel Barrett Ph.D. Alumni sbarrett [at] cs utexas edu
Julian Bishop Formerly affiliated Ph.D. Student julian [at] cs utexas edu
Ishan Durugkar Ph.D. Student ishand [at] cs utexas edu
Todd Hester Postdoctoral Alumni todd [at] cs utexas edu
Leif Johnson Ph.D. Alumni leif [at] cs utexas edu
Igor V. Karpov Masters Alumni ikarpov [at] gmail com
W. Bradley Knox Ph.D. Alumni bradknox [at] mit edu
Nate Kohl Ph.D. Alumni nate [at] natekohl net
Shailesh Kumar Masters Alumni
Elad Liebman Ph.D. Student eladlieb [at] cs utexas edu
Reza Mahjourian Ph.D. Alumni reza [at] cs utexas edu
Jacob Menashe Ph.D. Student jmenashe [at] cs utexas edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
Sanmit Narvekar Ph.D. Student sanmit [at] cs utexas edu
Aishwarya Padmakumar Ph.D. Alumni aish [at] cs utexas edu
Jefferson Provost Ph.D. Alumni jefferson provost [at] gmail com
Jefferson Provost Ph.D. Alumni jefferson provost [at] gmail com
Joseph Reisinger Ph.D. Alumni joeraii [at] cs utexas edu
Joseph Reisinger Formerly affiliated Ph.D. Student joeraii [at] cs utexas edu
Jacob Schrum Ph.D. Alumni schrum2 [at] southwestern edu
Peter Stone Faculty pstone [at] cs utexas edu
Nathaniel Tucker Undergraduate Alumni
Cem C Tutum Formerly affiliated Research Scientist tutum [at] cs utexas edu
Shimon Whiteson Formerly affiliated Collaborator s a whiteson [at] uva nl
Ruohan Zhang Ph.D. Student zharu [at] utexas edu
Publications
     [Expand to show all 269][Minimize]
Causal Policy Gradient for Whole-Body Mobile Manipulation 2023
Jiaheng Hu, Peter Stone, and Roberto Martin-Martin, In Robotics: Science and Systems (RSS2023), Daegu, Republic of Korea, July 2023.
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning 2023
Caroline Wang, Garrett Warnell, and Peter Stone, In Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS), London, UK, May 2023.
DM$^2$: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching 2023
Caroline Wang, Ishan Durugkar, Elad Liebman, and Peter Stone, In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), Washington, D.C., February 2023.
MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection 2023
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), Kigali, Rwanda, May 2023.
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning 2023
Bo Liu, Yihao Feng, Qiang Liu, and Peter Stone, In Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), Washington, DC, US, February 2023.
Model-Based Meta Automatic Curriculum Learning 2023
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 2023), Montreal, Canada, August 2023.
Reward (Mis)design for autonomous driving 2023
W. Bradley Knox, Alessandro Allievi, Holger Banzhaf, Felix Schmitt, and Peter Stone, Artificial Intelligence, Vol. 316 (2023).
The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications 2023
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), Washington, D.C., February 2023.
Adversarial Imitation Learning from Video using a State Observer 2022
Haresh Karnan, Garrett Warnell, Faraz Torabi, and Peter Stone, In International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, May 2022.
Causal Dynamics Learning for Task-Independent State Abstraction 2022
Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, and Peter Stone, In roceedings of the 39th International Conference on Machine Learning (ICML2022), Baltimore, USA, July 2022.
Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction 2022
Yulin Zhang, William Macke, Jiaxun Cui, Daniel Urieli, and Peter Stone, In Proceedings of the Adaptive and Learning Agents Workshop (ALA), Auckland, NZ, May 2022.
Model-Based Meta Automatic Curriculum Learning 2022
Zifan Xu, Yulin Zhang, Shahaf S. Shperberg, Reuth Mirsky, Yulin Zhan, Yuqian Jiang, Bo Liu, and Peter Stone, In Decision Awareness in Reinforcement Learning (DARL) workshop t the +39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, July 2022.
Task Factorization in Curriculum Learning 2022
Reuth Mirsky, Shahaf S. Shperberg, Yulin Zhang, Zifan Xu, Yuqian Jiang, Jiaxun Cui, and Peter Stone, In Decision Awareness in Reinforcement Learning (DARL) workshop t the 39th International Conference on Machine Learning (ICML), Baltimore, Maryland, USA, July 2022.
VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors 2022
Yifeng Zhu, Abhishek Joshi, Peter Stone, and Yuke Zhu, In Proceedings of the 6th Conference on Robot Learning (CoRL 2022), Auckland, New Zealand, January 2022.
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation 2022
Haresh Karnan, Garrett Warnell, Xuesu Xiao, and Peter Stone, In International Conference on Robotics and Automation, 2022, Philadelphia, Pennsylvania, May 2022.
A Scavenger Hunt for Service Robots 2021
Harel Yedidsion, Jennifer Suriadinata, Zifan Xu, Stefan Debruyn, and Peter Stone, In Proceedings of the 2021 International Conference on Robotics and Automation (ICRA 2021), Xi'an China, May 2021.
Adversarial Intrinsic Motivation for Reinforcement Learning 2021
Ishan Durugkar, Mauricio Tec, Scott Niekum, and Peter Stone, In Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021), Sydney, Australia, December 2021.
Capturing Skill State in Curriculum Learning for Human Skill Acquisition 2021
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), Virtual, September 2021.
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition 2021
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), Vienna, Austria, July 2021.
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation 2021
Faraz Torabi, Garrett Warnell, and Peter Stone, In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, September 2021.
Dialog Policy Learning for Joint Clarification and Active Learning Queries 2021
Aishwarya Padmakumar, Raymond J. Mooney, In The AAAI Conference on Artificial Intelligence (AAAI), Vol. , February 2021.
Generalization of Agent Behavior through Explicit Representation of Context 2021
Cem Tutum, Suhaib Abdulquddos, Risto Miikkulainen, In Proceedings of the 3rd IEEE Conference on Games, , 2021.
Grounded Action Transformation for Sim-to-Real Reinforcement Learning 2021
Josiah P.Hanna, Siddharth Desai, Haresh Karnan, Garrett Warnell, and Peter Stone, Special Issue on Reinforcement Learning for Real Life, Machine Learning, 2021 (2021).
Importance Sampling in Reinforcement Learning with an Estimated Behavior Policy 2021
Josiah P. Hanna, Scott Niekum, and Peter Stone, Machine Learning (MLJ), Vol. 110, 6 (2021), pp. 1267–1317.
Is the Cerebellum a Model-Based Reinforcement Learning Agent? 2021
Bharath Masetty, Reuth Mirsky, Ashish D. Deshpande, Michael Mauk, and Peter Stone, In Adaptive and Learning Agents Workshop at AAMAS, Virtual, May 2021.
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy 2021
Yunshu Du, Garrett Warnell, Assefaw Gebremedhin, Peter Stone, and Matthew E. Taylor, Neural Computing and Applications (2021).
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning 2021
Eddy Hudson, Garrett Warnell, and Peter Stone, In Autonomous Robots and Multirobot Systems Workshop at the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021), London, UK, May 2021.
Supervised Attention from Natural Language Feedback for Reinforcement Learning 2021
Clara Cecilia Cannon, Masters Thesis, Department of Computer Science, The University of Texas at Austin.
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks 2021
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), Virtual Conference, February 2021.
Using Natural Language to Aid Task Specification in Sequential Decision Making Problems 2021
Prasoon Goyal, Ph.D. Proposal.
Adapting to Unseen Environments through Explicit Representation of Context 2020
Cem C Tutum, Risto Miikkulainen, In Proceedings of the 2020 Conference on Artificial Life (ALIFE 2020), pp. 581--588, Montreal, Canada, July 2020. The MIT Press.
Agents teaching agents: a survey on inter-agent transfer learning 2020
Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, and Peter Stone, Autonomous Agents and Multi-Agent Systems (2020).
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch 2020
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), Virtual, December 2020.
Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning 2020
Ishan Durugkar, Elad Liebman, and Peter Stone, Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020) (2020).
Deep R-Learning for Continual Area Sweeping 2020
Rishi Shah, Yuqian Jiang, Justin Hart, and Peter Stone, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020) (2020).
Dialog as a Vehicle for Lifelong Learning of Grounded Language Understanding Systems 2020
Aishwarya Padmakumar, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription 2020
Olivier Francon, Santiago Gonzalez, Babak Hodjat, Elliot Meyerson, Risto Miikkulainen, Xin Qiu, Hormoz Shahrzad, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2020), 2020.
Evaluating the Robustness of Natural Language Reward Shaping Models to Spatial Relations 2020
Antony Yun, Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
On Sampling Error in Batch Action-Value Prediction Algorithms 2020
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., Remote (Virtual Conference), December 2020.
PixL2R: Guiding Reinforcement Learning using Natural Language by Mapping Pixels to Rewards 2020
Prasoon Goyal, Scott Niekum, Raymond J. Mooney, In 4th Conference on Robot Learning (CoRL), November 2020. Also presented on the 1st Language in Reinforcement Learning (LaReL) Workshop at ICML, July 2020 (Best Paper Award), the 6th Deep Rein...
Policy Evaluation in Continuous MDPs with Efficient Kernelized Gradient Temporal Difference 2020
Alec Koppel, Garrett Warnell, Ethan Stump, Peter Stone, and Alejandro Ribeiro, No other information
Reducing Sampling Error in Batch Temporal Difference Learning 2020
Brahma Pavse, Ishan Durugkar, Josiah Hanna, and Peter Stone, In Proceedings of the 37th International Conference on Machine Learning (ICML), Vienna, Austria (Virtual Conference), July 2020.
Reinforced Grounded Action Transformation for Sim-to-Real Transfer 2020
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.
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration 2020
Brahma Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone, IEEE Robotics and Automation Letters, presented at International Conference on Intelligent Robots and Systems (IROS) (2020).
Stochastic Grounded Action Transformation for Robot Learning in Simulation 2020
Siddharth Desai, Haresh Karnan, Josiah P. Hanna, Garrett Warnell, and Peter Stone, In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2020), Las Vegas, NV, USA, October 2020.
The EMPATHIC Framework for Task Learning from Implicit Human Feedback 2020
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), Cambridge MA, USA, November 2020.
Building Self-Play Curricula Online by Playing with Expert Agents in Adversarial Games 2019
Felipe Leno Da Silva, Anna Helena Reali Costa, and Peter Stone, In Proceedings of the 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Bahia, Brazil, October 2019.
Importance Sampling Policy Evaluation with an Estimated Behavior Policy 2019
Josiah Hanna, Scott Niekum, and Peter Stone, In Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California, U.S.A., June 2019.
Learning Curriculum Policies for Reinforcement Learning 2019
Sanmit Narvekar and Peter Stone, In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Montreal, Canada, May 2019.
Object-model transfer in the general video game domain 2019
Alexander Braylan, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin.
Reducing Sampling Error in Policy Gradient Learning 2019
Josiah Hanna and Peter Stone, In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Montreal, Canada, May 2019.
The right music at the right time: adaptive personalized playlists based on sequence modeling 2019
Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone Peter Stone, Management Information Systems Quarterly, Vol. 43, 3 (2019), pp. 765--786.
Using Natural Language for Reward Shaping in Reinforcement Learning 2019
Prasoon Goyal, Scott Niekum, Raymond J. Mooney, In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, August 2019.
Deep TAMER: Interactive agent shaping in high-dimensional state spaces 2018
Garrett Warnell, Nicholas Waytowich, Vernon Lawhern, and Peter Stone, In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2018.
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning 2018
Prabhat Nagarajan, Garrett Warnell, and Peter Stone, In 2nd Reproducibility in Machine Learning Workshop at ICML 2018, Stockholm, Sweden, July 2018.
Discovering Gated Recurrent Neural Network Architectures 2018
Aditya Rawal, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play 2018
Reza Mahjourian, PhD Thesis, University of Texas at Austin.
Learning a Policy for Opportunistic Active Learning 2018
Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney, In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.
State Abstraction Synthesis for Discrete Models of Continuous Domains 2018
Jacob Menashe and Peter Stone, In Data Efficient Reinforcement Learning Workshop at AAAI Spring Symposium, Stanford, CA, USA, March 2018.
Towards a Data Efficient Off-Policy Policy Gradient 2018
Josiah Hanna and Peter Stone, In AAAI Spring Symposium on Data Efficient Reinforcement Learning, Palo Alto, CA, March 2018.
A Stitch in Time - Autonomous Model Management via Reinforcement Learning 2018
Elad Liebman, Eric Zavesky, and Peter Stone, In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Stockholm, Sweden, July 2018.
Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning 2017
Sanmit Narvekar, Jivko Sinapov, and Peter Stone, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, August 2017.
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation 2017
Josiah Hanna, Peter Stone, and Scott Niekum, In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Sao Paolo, Brazil, May 2017.
Data-Efficient Policy Evaluation Through Behavior Policy Search 2017
Josiah Hanna, Philip Thomas, Peter Stone, and Scott Niekum, In Proceedings of the 34th International Conference on Machine Learning (ICML), Sydney, Australia, August 2017.
Designing Better Playlists with Monte Carlo Tree Search 2017
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), San Francisco, USA, February 2017.
Integrated Learning of Dialog Strategies and Semantic Parsing 2017
Aishwarya Padmakumar, Jesse Thomason, and Raymond J. Mooney, In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), pp. 547--557, Valencia, Spain, April 2017.
TD Learning with Constrained Gradients 2017
Ishan Durugkar and Peter Stone, In Proceedings of the Deep Reinforcement Learning Symposium, NIPS 2017, Long Beach, CA, USA, December 2017.
A synthesis of automated planning and reinforcement learning for efficient, robust decision-making 2016
Matteo Leonetti, Luca Iocchi, and Peter Stone, Artificial Intelligence, Vol. 241 (2016), pp. 103 - 130.
Constructing Game Agents Through Simulated Evolution 2016
Jacob Schrum and Risto Miikkulainen, In Encyclopedia of Computer Graphics and Games, Newton Lee (Eds.), pp. 1--10 2016. Springer.
Deep Imitation Learning for Parameterized Action Spaces 2016
Matthew Hausknecht, Yilun Chen, and Peter Stone, In AAMAS Adaptive Learning Agents (ALA) Workshop, Singapore, May 2016.
Deep Reinforcement Learning in Parameterized Action Space 2016
Matthew Hausknecht and Peter Stone, In Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, May 2016.
Evolving Deep LSTM-based Memory networks using an Information Maximization Objective 2016
Aditya Rawal and Risto Miikkulainen, To Appear In Genetic and Evolutionary Computation Conference (GECCO 2016), Colorado, USA 2016.
Grounded Semantic Networks for Learning Shared Communication Protocols 2016
Matthew Hausknecht and Peter Stone, In Deep Reinforcement Learning, NIPS Workshop, Barcelona, Spain, December 2016.
Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork 2016
Matthew Hausknecht, Prannoy Mupparaju, Sandeep Subramanian, Shivaram Kalyanakrishnan, and Peter Stone, In AAMAS Adaptive Learning Agents (ALA) Workshop, Singapore, May 2016.
Machines Are Becoming More Creative Than Humans 2016
Risto Miikkulainen, VentureBeat, Vol. 2016/04/03 (2016).
Making Friends on the Fly: Cooperating with New Teammates 2016
Samuel Barrett, Avi Rosenfeld, Sarit Kraus, and Peter Stone, Artificial Intelligence (2016).
On-Policy vs. Off-Policy Updates for Deep Reinforcement Learning 2016
Matthew Hausknecht and Peter Stone, In Deep Reinforcement Learning: Frontiers and Challenges, IJCAI Workshop, New York, July 2016.
Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution 2016
Jacob Schrum and Risto Miikkulainen, Evolutionary Computation, Vol. 24, 3 (2016), pp. 459--490. MIT Press.
Source Task Creation for Curriculum Learning 2016
Sanmit Narvekar, Jivko Sinapov, Matteo Leonetti, and Peter Stone, In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), Singapore, May 2016.
State Aggregation through Reasoning in Answer Set Programming 2016
Ginevra Gaudioso, Matteo Leonetti, and Peter Stone, In Proceedings of the IJCAI Workshop on Autonomous Mobile Service Robots (WSR 16), New York City, NY, USA, July 2016.
Autonomous Trading in Modern Electricity Markets 2015
Daniel Urieli, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Code and binaries available at: http://www.cs.utexas.edu/~urieli/thesis.
Deep Recurrent Q-Learning for Partially Observable MDPs 2015
Matthew Hausknecht and Peter Stone, In AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (AAAI-SDMIA15), Arlington, Virginia, USA, November 2015.
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation 2015
Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone, In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Istanbul, Turkey, May 2015.
Intrinsically motivated model learning for developing curious robots 2015
Todd Hester and Peter Stone, Artificial Intelligence (2015). Elsevier.
Monte Carlo Hierarchical Model Learning 2015
Jacob Menashe and Peter Stone, In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Istanbul, Turkey, May 2015.
Sensorimotor Embedding: A Developmental Approach to Learning Geometry 2015
Jeremy Stober, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
Solving Interleaved and Blended Sequential Decision-Making Problems through Modular Neuroevolution 2015
Jacob Schrum and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 345--352, Madrid, Spain, July 2015. Best Paper: Digital Entertainment and Arts.
Communicating with Unknown Teammates 2014
Samuel Barrett, Noa Agmon, Noam Hazon, Sarit Kraus, and Peter Stone, In Proceedings of the Twenty-First European Conference on Artificial Intelligence, August 2014.
Cooperating with Unknown Teammates in Robot Soccer 2014
Samuel Barrett and Peter Stone, In AAMAS Autonomous Robots and Multirobot Systems Workshop (ARMS 2014), May 2014.
Cooperating with Unknown Teammates in Robot Soccer 2014
Samuel Barrett and Peter Stone, In AAAI Workshop on Multiagent Interaction without Prior Coordination (MIPC 2014), July 2014.
Evolving Multimodal Behavior Through Modular Multiobjective Neuroevolution 2014
Jacob Schrum, PhD Thesis, The University of Texas at Austin. Tech Report TR-14-07.
Evolving Multimodal Behavior With Modular Neural Networks in Ms. Pac-Man 2014
Jacob Schrum and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014), pp. 325--332, Vancouver, BC, Canada, July 2014. Best Paper: Digital Entertainment and Arts.
TacTex'13: A Champion Adaptive Power Trading Agent 2014
Daniel Urieli and Peter Stone, In Proceedings of the Twenty-Eighth Conference on Artificial Intelligence (AAAI 2014), July 2014.
A Learning Agent for Heat-Pump Thermostat Control 2013
Daniel Urieli and Peter Stone, In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS'13), May 2013.
Cooperating with a Markovian Ad Hoc Teammate 2013
Doran Chakraborty and Peter Stone, In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.
Evolutionary Feature Evaluation for Online Reinforcement Learning 2013
Julian Bishop and Risto Miikkulainen, In Proceedings of 2013 IEEE Conference on Computational Intelligence and Games (CIG2013), pp. 267-275 2013.
Learning Exploration Strategies in Model-Based Reinforcement Learning 2013
Todd Hester, Manuel Lopes, and Peter Stone, In The Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.
Learning Non-Myopically from Human-Generated Reward 2013
W. Bradley Knox and Peter Stone, In In Proceedings of the International Conference on Intelligent User Interfaces (IUI), March 2013.
Model-Selection for Non-Parametric Function Approximation in Continuous Control Problems: A Case Study in a Smart Energy System 2013
Daniel Urieli and Peter Stone, In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD'13), September 2013.
Multiagent Learning in the Presence of Memory-Bounded Agents 2013
Doran Chakraborty and Peter Stone, Autonomous Agents and Multiagent Systems (JAAMAS) (2013). Springer.
Targeted Opponent Modeling of Memory-Bounded Agents 2013
Doran Chakraborty and Peter Stone, In Proceedings of the Adaptive Learning Agents Workshop (ALA), May 2013.
The Open-Source TEXPLORE Code Release for Reinforcement Learning on Robots 2013
Todd Hester and Peter Stone, In RoboCup-2013: Robot Soccer World Cup {XVII}, Sven Behnke and Arnoud Visser and Rong Xiong and Manuela Veloso (Eds.) 2013. Springer Verlag.
Training a Robot via Human Feedback: A Case Study 2013
W. Bradley Knox, Peter Stone, and Cynthia Breazeal, In Social Robotics, October 2013.
Accelerating Evolution via Egalitarian Social Learning 2012
Wesley Tansey, Eliana Feasley, and Risto Miikkulainen, In Proceedings of the 14th Annual Genetic and Evolutionary Computation Conference (GECCO 2012), Philadelphia, Pennsylvania, USA 2012.
Evolution of a Communication Code in Cooperative Tasks 2012
Aditya Rawal, Padmini Rajagopalan, Risto Miikkulainen and Kay Holekamp, In Artificial Life (13th International Conference on the Synthesis and Simulation of Living Systems), East Lansing, Michigan, USA 2012.
How Humans Teach Agents: A New Experimental Perspective 2012
W. Bradley Knox, Brian D. Glass, Bradley C. Love, W. Todd Maddox, and Peter Stone, International Journal of Social Robotics, Vol. 4 (2012), pp. 409-421. Springer Netherlands.
Intrinsically Motivated Model Learning for a Developing Curious Agent 2012
Todd Hester and Peter Stone, In Eleventh International Conference on Autonomous Agents and Multiagent Systems - Adaptive Learning Agents Workshop (AAMAS - ALA), June 2012.
Intrinsically Motivated Model Learning for a Developing Curious Agent 2012
Todd Hester and Peter Stone, In The Eleventh International Conference on Development and Learning (ICDL), Nov 2012.
Learning from feedback on actions past and intended 2012
W. Bradley Knox, Cynthia Breazeal, and Peter Stone, In In Proceedings of 7th ACM/IEEE International Conference on Human-Robot Interaction, Late-Breaking Reports Session (HRI 2012), March 2012.
Learning from Human-Generated Reward 2012
W. Bradley Knox, No other information
Reinforcement Learning from Human Reward: Discounting in Episodic Tasks 2012
W. Bradley Knox and Peter Stone, In In Proceedings of the 21st IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man), September 2012.
Reinforcement Learning with Human and MDP Reward 2012
W. Bradley Knox and Peter Stone, In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012), June 2012.
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains. 2012
Todd Hester, PhD Thesis, The University of Texas at Austin. Code available at: http://www.ros.org/wiki/rl-texplore-ros-pkg.
RTMBA: A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control 2012
Todd Hester, Michael Quinlan, and Peter Stone, In {IEEE} International Conference on Robotics and Automation (ICRA), May 2012.
TEXPLORE: Real-Time Sample-Efficient Reinforcement Learning for Robots 2012
Todd Hester and Peter Stone, Machine Learning (2012).
A modular reinforcement learning model for human visuomotor behavior in a driving task 2011
Brian Sullivan, Leif Johnson, Dana Ballard and Mary Hayhoe, Proceedings of the AISB 2011 Symposium on Architectures for Active Vision. (2011), pp. 33-40.
A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control 2011
Todd Hester, Michael Quinlan, and Peter Stone, No other information
An Introduction to Inter-task Transfer for Reinforcement Learning 2011
Matthew E. Taylor and Peter Stone, AI Magazine, Vol. 32, 1 (2011), pp. 15--34.
Characterizing Reinforcement Learning Methods through Parameterized Learning Problems 2011
Shivaram Kalyanakrishnan and Peter Stone, Machine Learning (2011).
Evolving Multimodal Networks for Multitask Games 2011
Jacob Schrum and Risto Miikkulainen, In Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2011), pp. 102--109, Seoul, South Korea, September 2011. IEEE. (Best Paper Award).
Human-Assisted Neuroevolution Through Shaping, Advice and Examples 2011
Igor V. Karpov, Vinod K. Valsalam and Risto Miikkulainen, In Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference (GECCO 2011), Dublin, Ireland, July 2011.
Learning and Using Models 2011
Todd Hester and Peter Stone, In Reinforcement Learning: State of the Art 2011.
On Learning with Imperfect Representations 2011
Shivaram Kalyanakrishnan and Peter Stone, In Proceedings of the 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, April 2011.
Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning 2011
Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter Stone, In {IEEE} Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), April 2011.
Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree 2011
Doran Chakraborty and Peter Stone, In Proceedings of the Twenty Eighth International Conference on Machine Learning (ICML'11), June 2011.
Understanding Human Teaching Modalities in Reinforcement Learning Environments: A Preliminary Report 2011
W. Bradley Knox and Peter Stone, In IJCAI 2011 Workshop on Agents Learning Interactively from Human Teachers (ALIHT), July 2011.
UT^2: Human-like Behavior via Neuroevolution of Combat Behavior and Replay of Human Traces 2011
Jacob Schrum, Igor V. Karpov and Risto Miikkulainen, In Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2011), pp. 329--336, Seoul, South Korea, September 2011. IEEE.
Combining Manual Feedback with Subsequent MDP Reward Signals for Reinforcement Learning 2010
W. Bradley Knox and Peter Stone, In Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), May 2010.
Convergence, Targeted Optimality and Safety in Multiagent Learning 2010
Doran Chakraborty and Peter Stone, In Proceedings of the Twenty-seventh International Conference on Machine Learning (ICML 2010), June 2010.
Evolving Agent Behavior In Multiobjective Domains Using Fitness-Based Shaping 2010
Jacob Schrum and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010), pp. 439--446, Portland, Oregon, July 2010.
Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration 2010
Tobias Jung and Peter Stone, In Proceedings of the European Conference on Machine Learning, September 2010.
Neuroevolution 2010
Risto Miikkulainen, In Encyclopedia of Machine Learning, New York 2010. Springer.
Online Model Learning in Adversarial Markov Decision Processes (Extended Abstract) 2010
Doran Chakraborty and Peter Stone, In Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1583–-1584, May 2010.
Real Time Targeted Exploration in Large Domains 2010
Todd Hester and Peter Stone, In Proceedings of the Ninth International Conference on Development and Learning (ICDL 2010), 2010 (Eds.), August 2010.
Structured Exploration for Reinforcement Learning 2010
Nicholas Kenneth Jong, No other information
Transfer Learning for Reinforcement Learning on a Physical Robot 2010
Samuel Barrett, Matthew E. Taylor, and Peter Stone, In Ninth International Conference on Autonomous Agents and Multiagent Systems - Adaptive Learning Agents Workshop (AAMAS - ALA), May 2010.
An Empirical Analysis of Value Function-Based and Policy Search Reinforcement Learning 2009
Shivaram Kalyanakrishnan and Peter Stone, In The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 749-756, Richland, SC, May 2009. International Foundation for Autonomous Agents and Multiagent Sy...
An Empirical Comparison of Abstraction in Models of Markov Decision Processes 2009
Todd Hester and Peter Stone, In Proceedings of the ICML/UAI/COLT Workshop on Abstraction in Reinforcement Learning, June 2009.
Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning 2009
Shimon Whiteson, Matthew E. Taylor, and Peter Stone, Journal of Autonomous Agents and Multi-Agent Systems, Vol. 21, 1 (2009), pp. 1-27.
Design Principles for Creating Human-Shapable Agents 2009
W. Bradley Knox, Ian Fasel, and Peter Stone, In AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers, March 2009.
Evolving Adaptive Intelligence: Using NeuroEvolution with Temporal Difference Methods in the Game Domain 2009
Nathaniel Tucker, Technical Report HR-09-04, Department of Computer Science, The University of Texas at Austin..
Feature Selection for Value Function Approximation Using Bayesian Model Selection 2009
Tobias Jung and Peter Stone, In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2009.
Generalized Domains for Empirical Evaluations in Reinforcement Learning 2009
Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter Stone, In ICML Workshop on Evaluation Methods for Machine Learning, June 2009. To appear..
Generalized Model Learning for Reinforcement Learning in Factored Domains 2009
Todd Hester and Peter Stone, In The Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2009.
Interactively Shaping Agents via Human Reinforcement: The TAMER Framework 2009
W. Bradley Knox and Peter Stone, In The Fifth International Conference on Knowledge Capture, September 2009.
Learning Complementary Multiagent Behaviors: A Case Study 2009
Shivaram Kalyanakrishnan and Peter Stone, In Proceedings of the RoboCup International Symposium 2009 2009. Springer Verlag.
Learning in Fractured Problems for Constructive Neural Network Algorithms 2009
Nate Kohl, PhD Thesis, Department of Computer Sciences, University of Texas at Austin.
Transfer Learning for Reinforcement Learning Domains: A Survey 2009
Matthew E. Taylor and Peter Stone, Journal of Machine Learning Research, Vol. 10, 1 (2009), pp. 1633-1685.
A General Purpose Task Specification Language for Bootstrap Learning 2008
Ian Fasel, Michael Quinlan, and Peter Stone, In AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers, March 2008.
Autonomous Transfer for Reinforcement Learning 2008
Matthew E. Taylor, Gregory Kuhlmann, and Peter Stone, In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 2008.
Competition Between Reinforcement Learning Methods in a Predator-Prey Grid World 2008
Jacob Schrum, Technical Report AI08-9, The University of Texas at Austin, Department of Computer Sciences.
From pixels to policies: a bootstrapping agent 2008
Jeremy Stober and Benjamin Kuipers, In Proceedings of the IEEE International Conference on Development and Learning 2008.
Instance-Based Action Models for Fast Action Planning 2008
Mazda Ahmadi and Peter Stone, In RoboCup-2007: Robot Soccer World Cup XI, Ubbo Visser and Fernando Ribeiro and Takeshi Ohashi and Frank Dellaert (Eds.), Vol. 5001, pp. 1-16, Berlin 2008. Springer Verlag.
Model-based Reinforcement Learning in a Complex Domain 2008
Shivaram Kalyanakrishnan, Peter Stone, and Yaxin Liu, In RoboCup-2007: Robot Soccer World Cup XI, Ubbo Visser and Fernando Ribeiro and Takeshi Ohashi and Frank Dellaert (Eds.), Vol. 5001, pp. 171-83, Berlin 2008. Springer Verlag.
Online Kernel Selection for Bayesian Reinforcement Learning 2008
Joseph Reisinger, Peter Stone, and Risto Miikkulainen, In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008.
Transferring Instances for Model-Based Reinforcement Learning 2008
Matthew E. Taylor, Nicholas K. Jong, and Peter Stone, In Machine Learning and Knowledge Discovery in Databases, Vol. 5212, pp. 488-505, September 2008.
Acquiring Evolvability through Adaptive Representations 2007
Joseph Reisinger and Risto Miikkulainen, In Proceeedings of the Genetic and Evolutionary Computation Conference, pp. 1045-1052 2007.
Adaptive Tile Coding for Value Function Approximation 2007
Shimon Whiteson, Matthew E. Taylor, and Peter Stone, Technical Report AI-TR-07-339, University of Texas at Austin.
Autonomous Learning of Stable Quadruped Locomotion 2007
Manish Saggar, Thomas D'Silva, Nate Kohl, and Peter Stone, In RoboCup-2006: Robot Soccer World Cup X, Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi (Eds.), Vol. 4434, pp. 98-109, Berlin 2007. Springer Verlag.
Batch Reinforcement Learning in a Complex Domain 2007
Shivaram Kalyanakrishnan and Peter Stone, In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 650-657, New York, NY, USA, May 2007. ACM.
Coevolution of Role-Based Cooperation in Multi-Agent Systems 2007
Chern Han Yong and Risto Miikkulainen, Technical Report AI07-338, Department of Computer Sciences, The University of Texas at Austin.
Cross-Domain Transfer for Reinforcement Learning 2007
Matthew E. Taylor and Peter Stone, In Proceedings of the Twenty-Fourth International Conference on Machine Learning, June 2007.
Empirical Studies in Action Selection for Reinforcement Learning 2007
Shimon Whiteson, Matthew E. Taylor, and Peter Stone, Adaptive Behavior, Vol. 15, 1 (2007), pp. 33-50.
Graph-Based Domain Mapping for Transfer Learning in General Games 2007
Gregory Kuhlmann and Peter Stone, In Proceedings of the 18th European Conference on Machine Learning, September 2007.
Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study 2007
Shivaram Kalyanakrishnan, Yaxin Liu, and Peter Stone, In RoboCup-2006: Robot Soccer World Cup X, Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi (Eds.), Vol. 4434, pp. 72-85, Berlin 2007. Springer Verlag.
IFSA: Incremental Feature-Set Augmentation for Reinforcement Learning Tasks 2007
Mazda Ahmadi, Matthew E. Taylor, and Peter Stone, In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
Model-Based Exploration in Continuous State Spaces 2007
Nicholas K. Jong and Peter Stone, In The Seventh Symposium on Abstraction, Reformulation, and Approximation, July 2007.
Model-Based Function Approximation for Reinforcement Learning 2007
Nicholas K. Jong and Peter Stone, In The Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
Reinforcement Learning in High-Diameter, Continuous Environments 2007
Jefferson Provost, PhD Thesis, Computer Sciences Department, University of Texas at Austin.
Representation Transfer for Reinforcement Learning 2007
Matthew E. Taylor and Peter Stone, In AAAI 2007 Fall Symposium on Computational Approaches to Representation Change during Learning and Development, November 2007.
Self-Organizing Distinctive State Abstraction Using Options 2007
Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen, In Proceedings of the 7th International Conference on Epigenetic Robotics 2007.
Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison 2007
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.
The Chin Pinch: A Case Study in Skill Learning on a Legged Robot 2007
Peggy Fidelman and Peter Stone, In RoboCup-2006: Robot Soccer World Cup X, Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi (Eds.), Vol. 4434, pp. 59-71, Berlin 2007. Springer Verlag.
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning 2007
Matthew E. Taylor, Peter Stone, and Yaxin Liu, Journal of Machine Learning Research, Vol. 8, 1 (2007), pp. 2125-2167.
Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning 2007
Matthew E. Taylor, Shimon Whiteson, and Peter Stone, In Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, May 2007.
Comparing Evolutionary and Temporal Difference Methods for Reinforcement Learning 2006
Matthew Taylor, Shimon Whiteson, and Peter Stone, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1321-28, July 2006.
Developing navigation behavior through self-organizing distinctive state abstraction 2006
Jefferson Provost, Benjamin J. Kuipers, and Risto Miikkulainen, Connection Science, Vol. 18 (2006), pp. 159-172.
Evolutionary Function Approximation for Reinforcement Learning 2006
Shimon Whiteson and Peter Stone, Journal of Machine Learning Research, Vol. 7 (2006), pp. 877-917.
Integration and Evaluation of Exploration-Based Learning in Games 2006
Igor V. Karpov, Thomas D'Silva, Craig Varrichio, Kenneth O. Stanley, Risto Miikkulainen, In Proceedings of the {IEEE} Symposium on Computational Intelligence and Games, Reno, NV 2006. IEEE.
Keepaway Soccer: From Machine Learning Testbed to Benchmark 2006
Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, and Yaxin Liu, In RoboCup-2005: Robot Soccer World Cup IX, Itsuki Noda and Adam Jacoff and Ansgar Bredenfeld and Yasutake Takahashi (Eds.), Vol. 4020, pp. 93-105, Berlin 2006. Springer Verlag.
Using Active Relocation to Aid Reinforcement Learning 2006
Lilyana Mihalkova and Raymond Mooney, In Prodeedings of the 19th International FLAIRS Conference (FLAIRS-2006), pp. 580-585, Melbourne Beach, FL, May 2006.
Value Function Transfer for General Game Playing 2006
Bikramjit Banerjee, Gregory Kuhlmann, and Peter Stone, In ICML workshop on Structural Knowledge Transfer for Machine Learning, June 2006.
Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping 2006
Yaxin Liu and Peter Stone, In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 415-20, July 2006.
Academic AI and Video Games: A Case Study of Incorporating Innovative Academic Research into a Video Game Prototype 2005
Aliza Gold, In Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games (CIG'05) 2005. Piscataway, NJ: IEEE.
Behavior Transfer for Value-Function-Based Reinforcement Learning 2005
Matthew E. Taylor and Peter Stone, In The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, Frank Dignum and Virginia Dignum and Sven Koenig and Sarit Kraus and Munindar P. Singh and Michael Woo...
Constructing Good Learners Using Evolved Pattern Generators 2005
Vinod K. Valsalam, James A. Bednar, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2005, H.-G. Beyer and others (Eds.), pp. 11-18 2005.
Effective Image Compression Using Evolved Wavelets 2005
Uli Grasemann and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference 2005.
Evolving Neural Network Agents in the NERO Video Game 2005
Kenneth O. Stanley, Bobby D. Bryant, and Risto Miikkulainen, In Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games (CIG'05), Piscataway, NJ 2005. IEEE.
Evolving Neural Network Ensembles for Control Problems 2005
David Pardoe, Michael Ryoo, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference 2005.
Function Approximation via Tile Coding: Automating Parameter Choice 2005
Alexander A. Sherstov and Peter Stone, In SARA 2005, J.-D. Zucker and I. Saitta (Eds.), Vol. 3607, pp. 194-205, Berlin 2005. Springer Verlag.
Incorporating Advice into Evolution of Neural Networks 2005
Chern Han Yong, Kenneth O. Stanley, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005) 2005. Late Breaking Papers.
Learning Basic Navigation for Personal Satellite Assistant Using Neuroevolution 2005
Yiu Fai Sit and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference 2005.
Neuroevolution of an Automobile Crash Warning System 2005
Kenneth Stanley, Nate Kohl, Rini Sherony, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference 2005.
Real-Time Learning in the NERO Video Game 2005
Kenneth O. Stanley, Ryan Cornelius, Risto Miikkulainen, Thomas D'Silva, and Aliza Gold, In Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2005) Demo Papers 2005.
Real-time Neuroevolution in the NERO Video Game 2005
Kenneth O. Stanley, Bobby D. Bryant, and Risto Miikkulainen, IEEE Transactions on Evolutionary Computation (2005), pp. 653-668. IEEE.
Reinforcement Learning for RoboCup-Soccer Keepaway 2005
Peter Stone, Richard S. Sutton, and Gregory Kuhlmann, Adaptive Behavior, Vol. 13, 3 (2005), pp. 165-188.
Retaining Learned Behavior During Real-Time Neuroevolution 2005
Thomas D'Silva, Roy Janik, Michael Chrien, Kenneth O. Stanley and Risto Miikkulainen, Artificial Intelligence and Interactive Digital Entertainment (2005). American Association for Artificial Intelligence.
Towards an Empirical Measure of Evolvability 2005
Joseph Reisinger, Kenneth O. Stanley, Risto Miikkulainen, In Genetic and Evolutionary Computation Conference {(GECCO2005)} Workshop Program, pp. 257-264, Washington, D.C. 2005. ACM Press.
Value Functions for RL-Based Behavior Transfer: A Comparative Study 2005
Matthew E. Taylor, Peter Stone, and Yaxin Liu, In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
Self-Organizing Perceptual and Temporal Abstraction for Robot Reinforcement Learning 2004
Jefferson Provost, Benjamin J. Kuipers and Risto Miikkulainen, In AAAI-04 Workshop on Learning and Planning in Markov Processes 2004.
Adaptive Job Routing and Scheduling 2004
Shimon Whiteson and Peter Stone, Engineering Applications of Artificial Intelligence, Vol. 17(7), 7 (2004), pp. 855-869. Corrected version.
Competitive Coevolution through Evolutionary Complexification 2004
Kenneth O. Stanley and Risto Miikkulainen, Journal of Artificial Intelligence Research, Vol. 21 (2004), pp. 63-100.
Efficient Allele Fitness Assignment with Self-organizing Multi-agent System 2004
Adrian Agogino and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004) Workshop Program, New York, NY 2004. Springer-Verlag.
Efficient Evolution of Neural Networks Through Complexification 2004
Kenneth O. Stanley, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
Evolving a Roving Eye for Go 2004
Kenneth O. Stanley and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Berlin 2004. Springer Verlag.
Evolving Reusable Neural Modules 2004
Joseph Reisinger, Kenneth O. Stanley, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference 2004.
Evolving Wavelets using a Coevolutionary Genetic Algorithm and Lifting 2004
Uli Grasemann and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 969-980, San Francisco 2004. Kaufmann.
Exploiting Morphological Conventions for Genetic Reuse 2004
Kenneth O. Stanley, Joseph Reisinger, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference ({GECCO}-2004) Workshop Program, Berlin 2004. Springer Verlag.
Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer 2004
Gregory Kuhlmann, Peter Stone, Raymond J. Mooney, and Jude W. Shavlik, In The AAAI-2004 Workshop on Supervisory Control of Learning and Adaptive Systems, July 2004.
Machine Learning for Fast Quadrupedal Locomotion 2004
Nate Kohl and Peter Stone, In Nineteenth National Conference on Artificial Intelligence, pp. 611-616, July 2004.
Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion 2004
Nate Kohl and Peter Stone, In Proceedings of the {IEEE} International Conference on Robotics and Automation, pp. 2619-2624, May 2004.
The Constructivist Learning Architecture: A Model of Cognitive Development for Robust Autonomous Robots 2004
Harold H. Chaput, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Also Technical Report TR-04-34.
Towards Learning to Ignore Irrelevant State Variables 2004
Nicholas K. Jong and Peter Stone, In The AAAI-2004 Workshop on Learning and Planning in Markov Processes -- Advances and Challenges 2004.
Transfer of Neuroevolved Controllers in Unstable Domains 2004
Faustino J. Gomez and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, Berlin 2004. Springer.
A Taxonomy for Artificial Embryogeny 2003
Kenneth O. Stanley and Risto Miikkulainen, Artificial Life, Vol. 9, 2 (2003), pp. 93-130.
Achieving High-Level Functionality through Evolutionary Complexification 2003
Kenneth O. Stanley and Risto Miikkulainen, In Proceedings of the AAAI-2003 Spring Symposium on Computational Synthesis, Stanford, CA 2003. AAAI Press.
Active Guidance for a Finless Rocket Using Neuroevolution 2003
Faustino J. Gomez and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 2084-2095, San Francisco 2003. Morgan Kaufmann.
Evolving Adaptive Neural Networks with and Without Adaptive Synapses 2003
Kenneth O. Stanley, Bobby D. Bryant, and Risto Miikkulainen, In Proceedings of the 2003 Congress on Evolutionary Computation, Piscataway, NJ 2003. IEEE.
Neuroevolution for Adaptive Teams 2003
Bobby D. Bryant and Risto Miikkulainen, In Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), pp. 2194-2201, Piscataway, NJ 2003. IEEE.
Robust Non-Linear Control through Neuroevolution 2003
Faustino J. Gomez, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
Utilizing Domain Knowledge in Neuroevolution 2003
James Fan, Raymond Lau, and Risto Miikkulainen, Proceedings of the Twentieth International Conference on Machine Learning (ICML-03, Washington, DC)
Adaptive Control Utilising Neural Swarming 2002
Alex v. E. Conradie, Risto Miikkulainen, and Christiaan Aldrich, In Proceedings of the Genetic and Evolutionary Computation Conference, William B. Langdon and Erick Cantu-Paz and Keith E. Mathias and Rajkumar Roy and David Davis and Riccardo Poli and Karth...
Continual Coevolution Through Complexification 2002
Kenneth O. Stanley and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), William B. Langdon and Erick Cantu-Paz and Keith E. Mathias and Rajkumar Roy and David Davis and Riccardo Pol...
Cultural Enhancement Of Neuroevolution 2002
Paul H. McQuesten, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI-02-295.
Efficient Evolution Of Neural Network Topologies 2002
Kenneth O. Stanley and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, William B. Langdon and Erick Cantu-Paz and Keith E. Mathias and Rajkumar Roy and David Davis and Riccardo Poli and Karthik...
Efficient Reinforcement Learning Through Evolving Neural Network Topologies 2002
Kenneth O. Stanley and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 9, San Francisco 2002. Morgan Kaufmann.
Eugenic Evolution Utilizing A Domain Model 2002
Matthew Alden, Aard-Jan van Kesteren, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 279-286 2002.
Evolving Neural Networks Through Augmenting Topologies 2002
Kenneth O. Stanley and Risto Miikkulainen, Evolutionary Computation, Vol. 10, 2 (2002), pp. 99-127.
Intelligent Process Control Utilizing Symbiotic Memetic Neuro-Evolution 2002
Alex v. E. Conradie, Risto Miikkulainen, and Christiaan Aldrich, In Proceedings of the 2002 Congress on Evolutionary Computation, pp. 6 2002.
Numerical Optimization With Neuroevolution 2002
Brian Greer, Henri Hakonen, Risto Lahdelma, and Risto Miikkulainen, In Proceedings of the 2002 Congress on Evolutionary Computation, pp. 361-401, Piscataway, NJ 2002. IEEE. Undergraduate Thesis, Department of Computer Sciences, The University of Texas at Aust...
The Dominance Tournament Method of Monitoring Progress in Coevolution 2002
Kenneth O. Stanley and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference ({GECCO}-2002) Workshop Program, pp. 7, San Francisco 2002. Morgan Kaufmann.
A Neuroevolution Method For Dynamic Resource Allocation On A Chip Multiprocessor 2001
Faustino J. Gomez, Doug Burger, and Risto Miikkulainen, In Proceedings of the {INNS-IEEE} International Joint Conference on Neural Networks, pp. 2355-2361, Piscataway, NJ 2001. IEEE.
A Social Reinforcement Learning Agent 2001
Charles Lee Isbell, Christian R. Shelton, Michael Kearns, Satinder Singh, and Peter Stone, In Proceedings of the Fifth International Conference on Autonomous Agents, pp. 377--384 2001.
Abrupt And Gradual Sound Change In An Expanding Lexicon 2001
Melissa A. Redford and Risto Miikkulainen, Technical Report AI01-289, Department of Computer Sciences, The University of Texas at Austin.
Applying ESP And Region Specialists To Neuro-Evolution For Go 2001
Andres Santiago Perez-Bergquist, Technical Report TR-01-24, Department of Computer Science, University of Texas at Austin.
Co-Evolving A Go-Playing Neural Network 2001
Alex Lubberts and Risto Miikkulainen, In Coevolution: {T}urning Adaptive Algorithms Upon Themselves, Birds-of-a-Feather Workshop, Genetic and Evolutionary Computation Conference ({GECCO}-2001), pp. 6 2001.
Constrained Emergence Of Universals And Variation In Syllable Systems 2001
Melissa A. Redford, Chun Chi Chen, and Risto Miikkulainen, Language and Speech (2001), pp. 27-56. Manuscript.
Cooperative Coevolution Of Multi-Agent Systems 2001
Chern Han Yong and Risto Miikkulainen, Technical Report AI07-338, Department of Computer Sciences, The University of Texas at Austin.
Creating Melodies With Evolving Recurrent Neural Networks 2001
Chun-Chi J. Chen and Risto Miikkulainen, In Proceedings of the {INNS-IEEE} International Joint Conference on Neural Networks, pp. 2241-2246, Piscataway, NJ 2001. IEEE.
Evolving Populations Of Expert Neural Networks 2001
Joseph Bruce and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 251-257, San Francisco, CA 2001. Morgan Kaufmann.
Eugenic Neuro-Evolution For Reinforcement Learning 2000
Daniel Polani and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pp. 1041-1046, San Francisco 2000. Morgan Kaufmann.
Neuro-Evolution And Natural Deduction 2000
Nirav S. Desai and Risto Miikkulainen, In Proceedings of The First {IEEE} Symposium on Combinations of Evolutionary Computation and Neural Networks, pp. 64-69, Piscataway, NJ 2000. IEEE.
Online Interactive Neuro-Evolution 2000
Adrian Agogino, Kenneth O. Stanley, and Risto Miikkulainen, Neural Processing Letters (2000), pp. 29-38.
TPOT-RL Applied to Network Routing 2000
Peter Stone, In Proceedings of the Seventeenth International Conference on Machine Learning, pp. 935-942 2000.
Confidence Based Dual Reinforcement Q-Routing: An Adaptive On-Line Routing Algorithm 1999
Shailesh Kumar and Risto Miikkulainen, In 16th International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 758--763, Stockholm, Sweden 1999. San Francisco, CA: Kaufmann.
Solving Non-Markovian Control Tasks With Neuroevolution 1999
Faustino J. Gomez and Risto Miikkulainen, In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1356-1361, San Francisco, CA 1999. Kaufmann.
Team-Partitioned, Opaque-Transition Reinforcement Learning 1999
Peter Stone and Manuela Veloso, In RoboCup-98: Robot Soccer World Cup II, Minoru Asada and Hiroaki Kitano (Eds.), Vol. 1604, pp. 261-72, Berlin 1999. Springer Verlag. Also in Proceedings of the Third International Confe...
2-D Pole Balancing With Recurrent Evolutionary Networks 1998
Faustino Gomez and Risto Miikkulainen, In Proceedings of the International Conference on Artificial Neural Networks (ICANN-98), pp. 425-430, Skovde, Sweden 1998. Berlin, New York: Springer.
Confidence Based Dual Reinforcement Q-Routing: An On-Line Adaptive Network Routing Algorithm 1998
Shailesh Kumar, Masters Thesis, Department of Computer Sciences, the University of Texas at Austin.. 108. Technical Report AI-98-267.
Confidence Based Q-Routing: An On-Line Adaptive Network Routing Algorithm 1998
Shailesh Kumar and Risto Miikkulainen, Smart Engineering Systems: Neural Networks, Fuzzy Logic, Data Mining, and Evolutionary ProgrammingC. H. Dagli and M. Akay and O. Ersoy and B. R. Fernandez and A. Smith (Eds.), Vol. 8 (1998).
Eugenic Evolution For Combinatorial Optimization 1998
John W. Prior, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. 126. Technical Report AI98-268.
Evolving Neural Networks To Play Go 1998
Norman Richards, David Moriarty, and Risto Miikkulainen, Applied IntelligenceThomas B{"a}ck (Eds.) (1998), pp. 768-775. San Francisco, CA: Morgan Kaufmann.
Hierarchical Evolution Of Neural Networks 1998
David E. Moriarty and Risto Miikkulainen, In Proceedings of the 1998 IEEE Conference on Evolutionary Computation (ICEC98), pp. 428-433, Anchorage, AK 1998. Piscataway, NJ: IEEE.
Modeling The Emergence Of Syllable Systems 1998
Melissa A. Redford, Chun Chi Chen, and Risto Miikkulainen, In Proceedings of the 20th Annual Conference of the Cognitive Science Society, Morton Ann Gernsbacher and Sharon J. Derry (Eds.), pp. 882-886 1998. Hillsdale, NJ: Erlbaum.
Culling And Teaching In Neuro-Evolution 1997
Paul McQuesten and Risto Miikkulainen, In Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA-97, East Lansing, MI), Thomas B{"a}ck (Eds.), pp. 760-767 1997. San Francisco, CA: Morgan Kaufmann.
Dual Reinforcement Q-Routing: An On-Line Adaptive Routing Algorithm 1997
Shailesh Kumar and Risto Miikkulainen, Smart Engineering Systems: Neural Networks, Fuzzy Logic, Data Mining, and Evolutionary ProgrammingC. H. Dagli, M. Akay, O. Ersoy, B. R. Fernandez and A. Smith (Eds.), Vol. 7 (1997).
Forming Neural Networks Through Efficient And Adaptive Coevolution 1997
David E. Moriarty and Risto Miikkulainen, Evolutionary Computation, Vol. 5 (1997), pp. 373--399.
Incremental Evolution Of Complex General Behavior 1997
Faustino Gomez and Risto Miikkulainen, Adaptive Behavior, 5 (1997), pp. 317-342.
Symbiotic Evolution Of Neural Networks In Sequential Decision Tasks 1997
David E. Moriarty, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. 117. Technical Report UT-AI97-257.
Efficient Reinforcement Learning Through Symbiotic Evolution 1996
David E. Moriarty and Risto Miikkulainen, Machine LearningLeslie Pack Kaelbling (Eds.), AI94-224 (1996), pp. 11-32.
Evolving Obstacle Avoidance Behavior In A Robot Arm 1996
David E. Moriarty and Risto Miikkulainen, In From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer and Jordan Pollack an...
On-Line Adaptation Of A Signal Predistorter Through Dual Reinforcement Learning 1996
Patrick Goetz, Shailesh Kumar and Risto Miikkulainen, In Machine Learning: Proceedings of the 13th Annual Conference (Bari, Italy), Lorenza Saitta (Eds.), pp. 175-181 1996. San Francisco, CA: Morgan Kaufmann.
Discovering Complex Othello Strategies Through Evolutionary Neural Networks 1995
David E. Moriarty and Risto Miikkulainen, Connection Science, Vol. 7 (1995), pp. 195--209.
Learning Sequential Decision Tasks 1995
David E. Moriarty and Risto Miikkulainen, Technical Report AI95-229, Department of Computer Sciences, The University of Texas at Austin.
Evolutionary Neural Networks For Value Ordering In Constraint Satisfaction Problems 1994
David E. Moriarty and Risto Miikkulainen, Technical Report AI94-218, Department of Computer Sciences, The University of Texas at Austin.
Evolving Neural Networks To Focus Minimax Search 1994
David E. Moriarty and Risto Miikkulainen, In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 1371-1377, Seattle, WA 1994. Cambridge, MA: MIT Press.
Grounding Robotic Control With Genetic Neural Networks 1994
Diane Law and Risto Miikkulainen, Technical Report AI94-223, Department of Computer Sciences, The University of Texas at Austin.
Searle, Subsymbolic Functionalism And Synthetic Intelligence 1994
Diane Law, Technical Report, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI94-222.
Using Marker-Based Genetic Encoding Of Neural Networks To Evolve Finite-State Behaviour 1991
Brad Fullmer and Risto Miikkulainen, In Toward a Practice of Autonomous Systems: {P}roceedings of the First {E}uropean Conference on Artificial Life, Francisco J. Varela and Paul Bourgine (Eds.), pp. 255-262, Cambridge, MA 1991. ...
Projects
The OpenNERO AI Research and Education Platform 2009 - Present
Learning Strategic Behavior in Sequential Decision Tasks 2009 - 2014
TEXPLORE: Real-Time Sample Efficient Reinforcement Learning 2009 - Present
Teaching an Agent Manually via Evaluative Reinforcement (TAMER) 2008 - Present
Controlling a Finless Rocket Through Neuroevolution 2002 - 2003
Adaptive Packet Routing: The Confidence-Based Dual Reinforcement Q-Learning Algorithm 1998 - 2000
Realtime Continuous Adaptive Behavior: The Rodney System 1997 - 1997
On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning 1995 - 1997
Software/Data
MM-NEAT Download at GitHub

Modular Multiobjective NEAT is a software fra... 		2014
BREVE Monsters BREVE is a system for designing Artificial Life simulations available at http://spiderlan... 2010
OpenNERO OpenNERO is a general research and education platform for artificial intelligence. The platform is based on a simulatio... 2010
Demos
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Labs
Neural Networks Machine Learning Learning Agents