Nicholas Jong
Ph.D. Alumni
Nick's dissertation examined the interplay between exploration and generalization in reinforcement learning, in particular the effects of structural assumptions and knowledge. To this end, his research integrates ideas in function approximation, hierarchical decomposition, and model-based learning. He has also worked at the IBM Watson Research Laboratory, applying ideas from reinforcement learning to challenging problems in the field of autonomic computing.
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Structured Exploration for Reinforcement Learning 2010
Nicholas Kenneth Jong,
Compositional Models for Reinforcement Learning 2009
Nicholas K. Jong and Peter Stone, In The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2009.
Hierarchical Model-Based Reinforcement Learning: Rmax + MAXQ 2008
Nicholas K. Jong and Peter Stone, In Proceedings of the Twenty-Fifth International Conference on Machine Learning, July 2008.
The Utility of Temporal Abstraction in Reinforcement Learning 2008
Nicholas K. Jong, Todd Hester, and Peter Stone, In The Seventh International Joint Conference on Autonomous Agents and Multiagent Systems, May 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.
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.
From Pixels to Multi-Robot Decision-Making: A Study in Uncertainty 2006
Peter Stone, Mohan Sridharan, Daniel Stronger, Gregory Kuhlmann, Nate Kohl, Peggy Fidelman, and Nicholas K. Jong, Robotics and Autonomous Systems, Vol. 54, 11 (2006), pp. 933-43. Special issue on Planning Under Uncertainty in Robotics..
Bayesian Models of Nonstationary Markov Decision Problems 2005
Nicholas K. Jong and Peter Stone, In IJCAI 2005 workshop on Planning and Learning in A Priori Unknown or Dynamic Domains, August 2005.
State Abstraction Discovery from Irrelevant State Variables 2005
Nicholas K. Jong and Peter Stone, In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, pp. 752-757, August 2005.
Towards autonomous topological place detection using the Extended Voronoi Graph 2005
Patrick Beeson, Nicholas K. Jong, and Benjamin Kuipers, In IEEE International Conference on Robotics and Automation (ICRA-05) 2005.
The UT Austin Villa 2003 Four-Legged Team 2004
Peter Stone, Kurt Dresner, Selim T. Erdougan, Peggy Fidelman, Nicholas K. Jong, Nate Kohl, Gregory Kuhlmann, Ellie Lin, Mohan Sridharan, Daniel Stronger, and Gurushyam Hariharan, In RoboCup-2003: Robot Soccer World Cup VII, Daniel Polani and Brett Browning and Andrea Bonarini and Kazuo Yoshida (Eds.), Berlin 2004. Springer Verlag.
The UT Austin Villa 2004 RoboCup Four-Legged Team: Coming of Age 2004
Peter Stone, Kurt Dresner, Peggy Fidelman, Nicholas K. Jong, Nate Kohl, Gregory Kuhlmann, Mohan Sridharan, and Daniel Stronger, Technical Report UT-AI-TR-04-313, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory.
Towards Employing PSRs in a Continuous Domain 2004
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.
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.
Learning Predictive State Representations 2003
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.
Formerly affiliated with Learning Agents