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MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

2022-06-20 08:12:26
Jeewon Jeon, Woojun Kim, Whiyoung Jung, Youngchul Sung
arXiv_AI
           
Abstract

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 subgoals 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.

Abstract (translated)

URL

https://arxiv.org/abs/2206.10607

PDF

https://arxiv.org/pdf/2206.10607.pdf

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