Shimon Whiteson
Neural Networks Lab: Formerly affiliated Collaborator
Learning Agents Lab: Ph.D. Alumni
Shimon's research is primarily focused on single- and multi-agent decision-theoretic planning and learning, especially reinforcement learning, though he is also interested in stochastic optimization methods such as neuroevolution. Current research efforts include comparing disparate approaches to reinforcement learning, developing more rigorous frameworks for empirical evaluations, improving the scalability of multiagent planning, and applying learning methods to traffic management, helicopter control, and data filtering in high energy physics.
Homepage:
http://staff.science.uva.nl/~whiteson
Publications
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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.
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.
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..
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.
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.
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.
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.
Evolutionary Function Approximation for Reinforcement Learning 2006
Shimon Whiteson and Peter Stone, Journal of Machine Learning Research, Vol. 7 (2006), pp. 877-917.
On-Line Evolutionary Computation for Reinforcement Learning in Stochastic Domains 2006
Shimon Whiteson and Peter Stone, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1577-84, July 2006.
Sample-Efficient Evolutionary Function Approximation for Reinforcement Learning 2006
Shimon Whiteson and Peter Stone, In {AAAI} 2006: {P}roceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 518-523, July 2006.
Automatic Feature Selection via Neuroevolution 2005
Shimon Whiteson, Peter Stone, Kenneth O. Stanley, Risto Miikkulainen, and Nate Kohl, In Proceedings of the Genetic and Evolutionary Computation Conference, June 2005.
Evolving Keepaway Soccer Players through Task Decomposition 2005
Shimon Whiteson, Nate Kohl, Risto Miikkulainen, and Peter Stone, Machine Learning, Vol. 59, 1 (2005), pp. 5-30.
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.
Concurrent Layered Learning 2003
Shimon Whiteson and Peter Stone, In {AAMAS} 2003: {P}roceedings of the Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, Jeffrey S. Rosenschein and Tuomas Sandholm and Michael Wooldridge and...
Projects
Cooperative Coevolution of Multi-Agent Systems 2000 - 2007
NEAT: Evolving Increasingly Complex Neural Network Topologies 2000 - 2011
Areas of Interest
Game Playing Neuroevolution Reinforcement Learning
Labs
Formerly affiliated with Learning Agents Formerly affiliated with Neural Networks