PAGNet: Pluggable Adaptive Generative Networks for Information Completion in Multi-Agent Communication

TL;DR

PAGNet introduces a pluggable generative framework for multi-agent reinforcement learning that improves communication efficiency and decision-making by synthesizing global state representations from local observations.

Contribution

The paper presents PAGNet, a novel framework integrating generative models into MARL to enhance communication and reduce computational costs.

Findings

Significant performance improvements across benchmarks.

Effective synthesis of global state representations.

Analysis of emergent communication patterns.

Abstract

For partially observable cooperative tasks, multi-agent systems must develop effective communication and understand the interplay among agents in order to achieve cooperative goals. However, existing multi-agent reinforcement learning (MARL) with communication methods lack evaluation metrics for information weights and information-level communication modeling. This causes agents to neglect the aggregation of multiple messages, thereby significantly reducing policy learning efficiency. In this paper, we propose pluggable adaptive generative networks (PAGNet), a novel framework that integrates generative models into MARL to enhance communication and decision-making. PAGNet enables agents to synthesize global states representations from weighted local observations and use these representations alongside learned communication weights for coordinated decision-making. This pluggable approach reduces the computational demands typically associated with the joint training of communication and policy networks. Extensive experimental evaluations across diverse benchmarks and communication scenarios demonstrate the significant performance improvements achieved by PAGNet. Furthermore, we analyze the emergent communication patterns and the quality of generated global states, providing insights into operational mechanisms.