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IJCAI
2007

Heuristic Selection of Actions in Multiagent Reinforcement Learning

8 years 7 months ago
Heuristic Selection of Actions in Multiagent Reinforcement Learning
This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the wellknown 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.
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna Helena Reali Costa
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