Interference-Aware K-Step Reachable Communication in Multi-Agent Reinforcement Learning

TL;DR

This paper introduces IA-KRC, a novel communication framework for multi-agent reinforcement learning that improves cooperation efficiency by considering physical reachability and interference prediction, leading to better performance in complex environments.

Contribution

The paper proposes IA-KRC, combining a K-step reachability protocol with an interference prediction module to enhance communication effectiveness in multi-agent systems.

Findings

IA-KRC outperforms existing methods in complex scenarios.

It demonstrates increased robustness and scalability.

The framework reduces interference and improves cooperation persistence.

Abstract

Effective communication is pivotal for addressing complex collaborative tasks in multi-agent reinforcement learning (MARL). Yet, limited communication bandwidth and dynamic, intricate environmental topologies present significant challenges in identifying high-value communication partners. Agents must consequently select collaborators under uncertainty, lacking a priori knowledge of which partners can deliver task-critical information. To this end, we propose Interference-Aware K-Step Reachable Communication (IA-KRC), a novel framework that enhances cooperation via two core components: (1) a K-Step reachability protocol that confines message passing to physically accessible neighbors, and (2) an interference-prediction module that optimizes partner choice by minimizing interference while maximizing utility. Compared to existing methods, IA-KRC enables substantially more persistent and efficient cooperation despite environmental interference. Comprehensive evaluations confirm that IA-KRC achieves superior performance compared to state-of-the-art baselines, while demonstrating enhanced robustness and scalability in complex topological and highly dynamic multi-agent scenarios.