Behavior acquisition based on multi-module learning system in multi-agent environment

The conventional reinforcement learning approaches have difficulties to handle the policy alternation of the opponents because it may cause dynamic changes of state transition probabilities of which stability is necessary for the learning to converge. This paper presents a method of multi-module reinforcement learning in a multiagent environment, by which the learning agent can adapt itself to the policy changes of the opponents. We show a preliminary result of a simple soccer situation in the context of RoboCup.