基于随机博弈的Agent协同强化学习方法

A Cooperative Reinforcement Learning Method Based on the Stochastic Game in Multi-Agent Systems

本文针对一类追求系统得益最大化的协作团队的学习问题,基于随机博弈的思想,提出了一种新的多Agent协同强化学习方法。协作团队中的每个Agent通过观察协作相识者的历史行为,依照随机博弈模型预测其行为策略,进而得出最优的联合行为策略。

This paper aims at the learning process of a kind of cooperative teams, which pursue the maximum benefit of a whole system. We propose a new cooperative reinforcement learning method based on the stochastic game in multi-agent systems. Each agent of the team decides its behaviors after forecasting the behavior strategy of acquaintances according to the stochastic game structure and their historical behaviors, and then a jointly optimal behavior strategy is obtained.