MULTI-AGENT LEARNING BASED ON GENERAL-SUM STOCHASTIC GAMES
基于一般和随机对策论框架下的多智能体学习

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.