MULTI-AGENT LEARNING BASED ON GENERAL-SUM STOCHASTIC GAMES
Ou Hai · 2002
Q -learning from original single-agent framework is extended to non-cooperative multi-agent framework, and the theoretic framework of multi-agent learning is proposed under general-sum stochastic games with Nash equilibrium point as learning objective. We introduce a multi-agent Q -learning algorithm and prove its convergence under certain restriction, which is very important for the study and application of multi-agent system.