The conventional reinforcement learning approaches have difficulties in handling 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. A multiple learning module approach would provide one solution for this problem. If we can assign multiple learning modules to different situations in which each of the module can regard the state transition probabilities as consistent, then the system would provide reasonable performance. This paper presents a method of multi-module reinforcement learning in a multi-agent environment, by which the learning agent can adapt its behaviors to the situations as results of the other agent's behaviors. We show a preliminary result of a simple soccer situation.
['Yasutake Takahashi', 'Kazuhiro Edazawa', 'Minoru Asada']
Multi-module learning system for behavior acquisition in multi-agent environment