$f$-Divergence Policy Optimization in Fully Decentralized Cooperative MARL

ICLR 2025 Conference Submission3158 Authors

Keywords: multi-agent, reinforcement learning, fully decentralized learning, policy optimization, convergence, independent learning

Abstract: Independent learning is a straightforward solution for fully decentralized learning in cooperative multi-agent reinforcement learning (MARL). The study of independent learning has a history of decades, and the representatives, such as independent Q-learning and independent PPO, can obtain good performance in some benchmarks. However, most independent learning algorithms lack convergence guarantees or theoretical support. In this paper, we propose a general formulation of independent policy optimization, $f$-divergence policy optimization. We show the generality of such a formulation and analyze its limitation. Based on this formulation, we further propose a novel independent learning algorithm, TVPO, that theoretically guarantees convergence. Empirically, we show that TVPO outperforms state-of-the-art fully decentralized learning methods in three popular cooperative MARL benchmarks, which verifies the efficacy of TVPO.

Primary Area: reinforcement learning

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Submission Number: 3158