What question did this study set out to answer?

The aim is to address the dilemma in cooperative multiagent reinforcement learning between exploring new actions and exploiting known rewarding actions.

March 23, 2026

Learning Optimal Policies With Local Observations for Cooperative Multiagent Reinforcement Learning

Key Points

The aim is to address the dilemma in cooperative multiagent reinforcement learning between exploring new actions and exploiting known rewarding actions.
Theoretically proving the existence of a latent state ensuring optimal policies.
Proposing a unified approach (UMARL) that combines exploration and exploitation.
Designing agent representation network (ARN) and individual weighting networks (IWNs) for unified representations.
Implementing a latent state regularizer (LSR) to guide agent representations.
UMARL outperformed 12 state-of-the-art methods in various environments.
Superior performance demonstrated on m-step matrix game, level-based foraging, StarCraft II, and Google research football.
Experiments validate the effectiveness of using local observations to approximate latent states.

Abstract

The cooperative multiagent reinforcement learning (MARL) has been widely used in many practical applications. Despite its success, a fundamental issue arises in MARL that agents face the dilemma of whether to select the best action to maximize rewards or to acquire more information collectively by exploring the novel states/actions due to partial observability. To solve this issue, existing methods merge exploration and exploitation methods. However, these methods are always suboptimal and may lead to failure in finishing tasks. In this article, we theoretically prove the existence of a latent state that can guarantee the optimal individual and global policies. Moreover, we prove that such a latent state can be approximately obtained by local observations. Based on the analysis, we propose a method named unified MARL (UMARL), which is a weighted value function factorization approach unifying exploitation and exploration in one framework. Specifically, we design the agent representation network (ARN) and individual weighting networks (IWNs) to learn agents' unified representations and weights of credit. Moreover, a latent state regularizer (LSR) is designed to encourage agents' representations to approximate the latent state. Extensive experiments show that UMARL can achieve superior performance compared with 12 state-of-the-art methods on m -step matrix game, level-based foraging (LBF), StarCraft II, and Google research football (GRF). The source code is available at: https: //github. com/CrazyBayes/UMARL.

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