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MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
JEON JEEWON · WOOJUN KIM · Whiyoung Jung · Youngchul Sung

Wed Jul 20 10:55 AM -- 11:00 AM (PDT) @ None

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 actionable representation relevant to Q-learning so that the agents reach their sub-goals while maximizing the joint action value. Numerical results show that MASER significantly outperforms StarCraft II micromanagement benchmark compared to other state-of-the-art MARL algorithms.

Author Information

JEON JEEWON (Korea Advanced Institute of Science and Technology)
WOOJUN KIM (KAIST)
Whiyoung Jung (KAIST)
Youngchul Sung (KAIST)

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