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Cooperative behavior acquistion by asynchronous policy renewal that enables simultaneous learning in multiagent enviroment

This paper presents a method for simultaneous learning in multiagent environment to emerge the cooperative behaviors. Each agent has one policy and one action value function: the former is for action execution based on the the action value function updated in the previous stage, and the latter is for learning based on the episodes experienced by the 2-greedy method. This makes all agents behave based on the fixed policies, by which the non-Markovian problem can be avoided except for the update periods that depends on the learning progress of each agent. In order to avoid the local maxima due to such asynchronous renewal of action value functions, optimistic action values are given as initial ones, that helps the exploration process not to be trapped in the local maxima. The experimental results applied to one of the cooperative task in dynamic, multiagent environment, RoboCup, is shown and a discussion is given.