Simultaneous Learning to Acquire Competitive Behaviors in Multi-agent System Based on Modular Learning System

The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is a case of RoboCup competitions since other agent behaviors may cause sudden changes in state transition probabilities of 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

• Type: Conference
• Year: 2005
• Where: ROBOCUP
• Authors: Yasutake Takahashi, Kazuhiro Edazawa, Kentarou Noma, Minoru Asada
• Keywords: Multiagent Dynamic Environments, Policy Alternation, Reinforcement Learning Approaches, ROBOCUP 2005