Multi-agents reinforcement learning for symmetrical coordination
Wei Han · Computer Engineering and Applications Journal · 2008
Considering the problem of robots coordination games,the paper puts forward an agents’belief revision model and a learning algorithm Position-Exchanging Learning(PEL) which is based on the similarity of agents’strategies in coordination games. By position-exchanging,each agent stands from the viewpoint of its opponent and infers opponents’actions.The belief revision model combines the objective observed actions and subjective inferred actions.Coordination is assured by adjusting the belief de- gree to be 0 or 1.The algorithm PEL is tested in simulations that robots coordinate to avoid collision,and the results prove it performs better than present methods.