C4.5: programs for machine learning
C4.5: programs for machine learning
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Brains, Behavior and Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Keeping the Ball from CMUnited-99
RoboCup 2000: Robot Soccer World Cup IV
Refinement of Soccer Agents' Positions Using Reinforcement Learning
RoboCup-97: Robot Soccer World Cup I
Team-Partitioned, Opaque-Transition Reinforced Learning
RoboCup-98: Robot Soccer World Cup II
The CMUnited-99 Champion Simulator Team
RoboCup-99: Robot Soccer World Cup III
The RoboCup synthetic agent challenge 97
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2001: Robot Soccer World Cup V
Comparison of Several Machine Learning Techniques in Pursuit-Evasion Games
RoboCup 2001: Robot Soccer World Cup V
Reinforcement learning for cooperative actions in a partially observable multi-agent system
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Multi-agent reinforcement learning for intrusion detection
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
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As a sequential decision problem, robotic soccer can benefit from research in reinforcement learning. We introduce the 3 vs. 2 keepaway domain, a subproblem of robotic soccer implemented in the RoboCup soccer server. We then explore reinforcement learning methods for policy evaluation and action selection in this distributed, real-time, partially observable, noisy domain. We present empirical results demonstrating that a learned policy can dramatically outperform hand-coded policies.