Sample-Efficient Multi-Agent RL: An Optimization Perspective

• URL: http://arxiv.org/abs/2310.06243v1
• Date: Tue, 10 Oct 2023 01:39:04 GMT
• Title: Sample-Efficient Multi-Agent RL: An Optimization Perspective
• Authors: Nuoya Xiong, Zhihan Liu, Zhaoran Wang, Zhuoran Yang
• Abstract summary: We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. We introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. We show that our algorithm provides comparable sublinear regret to the existing works.
• Score: 103.35353196535544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. In order to find the minimum assumption for sample-efficient learning, we introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. Using this measure, we propose the first unified algorithmic framework that ensures sample efficiency in learning Nash Equilibrium, Coarse Correlated Equilibrium, and Correlated Equilibrium for both model-based and model-free MARL problems with low MADC. We also show that our algorithm provides comparable sublinear regret to the existing works. Moreover, our algorithm combines an equilibrium-solving oracle with a single objective optimization subprocedure that solves for the regularized payoff of each deterministic joint policy, which avoids solving constrained optimization problems within data-dependent constraints (Jin et al. 2020; Wang et al. 2023) or executing sampling procedures with complex multi-objective optimization problems (Foster et al. 2023), thus being more amenable to empirical implementation.

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