A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning

Woojun Kim; Whiyoung Jung; Myungsik Cho; Youngchul Sung

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arXiv · 2006.02732

A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning

Woojun Kim·Whiyoung Jung·Myungsik Cho·Youngchul Sung

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Abstract

In this paper, we propose a maximum mutual information (MMI) framework for multi-agent reinforcement learning (MARL) to enable multiple agents to learn coordinated behaviors by regularizing the accumulated return with the mutual information between actions. By introducing a latent variable to induce nonzero mutual information between actions and applying a variational bound, we derive a tractable lower bound on the considered MMI-regularized objective function. Applying policy iteration to maximize the derived lower bound, we propose a practical algorithm named variational maximum mutual information multi-agent actor-critic (VM3-AC), which follows centralized learning with decentralized execution (CTDE). We evaluated VM3-AC for several games requiring coordination, and numerical results show that VM3-AC outperforms MADDPG and other MARL algorithms in multi-agent tasks requiring coordination.

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BibTeX RIS

Woojun Kim, Whiyoung Jung, Myungsik Cho, Youngchul Sung. 2020-06-04. A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2006.02732

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