Simultaneous learning to acquire competitive behaviors in multi-agent system based on a modular learning system
Yasutake Takahashi, Kazuhiro Edazawa, Kentarou Noma, Minoru Asada · 2005
Existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is the case of RoboCup competitions because other agent behaviors may cause sudden changes in state transition probabilities in which constancy is needed for the learning to converge. The keys for simultaneous learning to acquire competitive behaviors in such an environment are: a modular learning system for adaptation to the policy alternation of others; and an introduction of macro actions for simultaneous learning to reduce the search space. This paper presents a method of modular learning in a multiagent environment in which the learning agents can simultaneously learn their behaviors and adapt themselves to the situations as a consequence of the others' behaviors.