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Coordination through Mutual Notification in Cooperative Multiagent Reinforcement Learning

机译:相互通知中的协作式多主体强化学习中的协调

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We present a new algorithm for cooperative reinforcement learning in multiagent systems. Our main concern is the correct coordination between the members of the team: We seek to obtain an optimal solution for the team as a whole while keeping the learning as much decentralized as possible. We consider autonomous and independently learning agents that do not store any explicit information about their teammates behavior, as well as possibly different reward functions for each agent. Coordination between agents occurs through communication, namely the mutual notification algorithm.
机译:我们提出了一种用于多主体系统中的协同强化学习的新算法。我们主要关注的是团队成员之间的正确协调:我们寻求为整个团队获得最佳解决方案,同时使学习尽可能分散。我们考虑自主且独立学习的代理,这些代理不会存储有关队友行为的任何明确信息,也不会为每个代理存储不同的奖励功能。代理之间的协调是通过通信进行的,即相互通知算法。

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