Heuristic selection of actions in multiagent reinforcement learning
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40
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2007
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Bianchi R.A.C.
Ribeiro C.H.C.
Costa A.H.R.
Ribeiro C.H.C.
Costa A.H.R.
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IJCAI International Joint Conference on Artificial Intelligence
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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.