Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

TL;DR

This paper introduces a novel team regret minimization approach for multi-agent deep reinforcement learning, improving cooperation in partially observable environments through decentralized policies and state estimation.

Contribution

It proposes a new team regret minimization method, a way to decompose team regret for decentralized execution, and employs a differential particle filter for better state estimation.

Findings

Outperforms state-of-the-art methods in cooperative and mixed games.

Effective in partially observable environments.

Enhances sample efficiency and cooperation among agents.

Abstract

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme,where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently. How-ever, an issue remains open: in the centralized training process,when the environment for the team is partially observable ornon-stationary, i.e., the observation and action informationof all the agents cannot represent the global states, existingmethods perform poorly and sample inefficiently. Regret Min-imization (RM) can be a promising approach as it performswell in partially observable and fully competitive settings.However, it tends to model others as opponents and thus can-not work well under the CTDE scheme. In this work, wepropose a novel team RM based Bayesian MARL with threekey contributions: (a) we design a novel RM method to traincooperative agents as a team and obtain a team regret-basedpolicy for that team; (b) we introduce a novel method to de-compose the team regret to generate the policy for each agentfor decentralized execution; (c) to further improve the perfor-mance, we leverage a differential particle filter (a SequentialMonte Carlo method) network to get an accurate estimation ofthe state for each agent. Experimental results on two-step ma-trix games (cooperative game) and battle games (large-scalemixed cooperative-competitive games) demonstrate that ouralgorithm significantly outperforms state-of-the-art methods.