Sample-Efficient Multi-Agent RL: An Optimization Perspective
This work addresses the challenge of sample efficiency in multi-agent reinforcement learning for researchers and practitioners, offering a more empirically implementable solution compared to prior methods.
The paper tackles the problem of sample-efficient multi-agent reinforcement learning in general-sum Markov Games by introducing the Multi-Agent Decoupling Coefficient (MADC) as a complexity measure, resulting in a unified algorithmic framework that ensures sample efficiency for learning various equilibria with low MADC and provides comparable sublinear regret.
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.