Heuristic selection of actions in multiagent reinforcement learning

Authors:
Reinaldo A. C. Bianchi;Carlos H. C. Ribeiro;Anna H. R. Costa
Affiliations:
FEI University Center, Electrical Engineering Department, São Bernardo do Campo, Brazil;Technological Institute of Aeronautics, Computer Science Division, São José dos Campos, Brazil;Escola Politécnica, University of São Paulo, São Paulo, Brazil
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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Abstract

This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the well-known Multiagent Reinforcement Learning algorithm Minimax-Q. A heuristic function H that influences the choice of the actions characterises the HAMMQ algorithm. This function is associated with a preference policy that indicates that a certain action must be taken instead of another. A set of empirical evaluations were conducted for the proposed algorithm in a simplified simulator for the robot soccer domain, and experimental results show that even very simple heuristics enhances significantly the performance of the multiagent reinforcement learning algorithm.