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MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer. (arXiv:2206.10607v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
In this paper, we consider cooperative multi-agent reinforcement learning
(MARL) with sparse reward. To tackle this problem, we propose a novel method
named MASER: MARL with subgoals generated from experience replay buffer. Under
the widely-used assumption of centralized training with decentralized execution
and consistent Q-value decomposition for MARL, MASER automatically generates
proper subgoals for multiple agents from the experience replay buffer by
considering both individual Q-value and total Q-value. Then, MASER designs
individual intrinsic reward for each agent based on …
arxiv experience generated learning lg reinforcement reinforcement learning