Learning to Perceive and Act by Trial and Error
Machine Learning
Temporal difference learning and TD-Gammon
Communications of the ACM
Learning to Play Chess Using Temporal Differences
Machine Learning
Statistical Reasoning Strategies in the Pursuit and Evasion Domain
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Team-Partitioned, Opaque-Transition Reinforced Learning
RoboCup-98: Robot Soccer World Cup II
Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent's Internal Model
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
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We introduce a novel reinforcement learning method for multiagent systems called N-learning. It has been developed to deal with the state space explosion caused by the presence of additional agents in an environment. N-learning is applied to a pursuit-evasion problem where a pursuer aims to calculate optimal policies for the interception of a deterministically moving evader, using an action selection component that can be realised through a number of techniques, and a heuristic reinforcement learning reward function. It is demonstrated that N-learning is able to outperform Q-learning at the pursuit-evasion task.