FMRQ—A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks
提出一种名为FMRQ的多智能体强化学习算法，用于完全合作任务，通过使用最高全局即时奖励的频率作为强化信号，使每个智能体无需观察其他智能体的动作，仅共享状态和奖励即可收敛到最优纳什均衡，在重复博弈和多项任务中表现优于其他算法。
In this paper, we propose a multiagent reinforcement learning algorithm dealing with fully cooperative tasks. The algorithm is called frequency of the maximum reward Q-learning (FMRQ). FMRQ aims to achieve one of the optimal Nash equilibria so as to optimize the performance index in multiagent systems. The frequency of obtaining the highest global immediate reward instead of immediate reward is used as the reinforcement signal. With FMRQ each agent does not need the observation of the other agents' actions and only shares its state and reward at each step. We validate FMRQ through case studies of repeated games: four cases of two-player two-action and one case of three-player two-action. It is demonstrated that FMRQ can converge to one of the optimal Nash equilibria in these cases. Moreover, comparison experiments on tasks with multiple states and finite steps are conducted. One is box-pushing and the other one is distributed sensor network problem. Experimental results show that the proposed algorithm outperforms others with higher performance.