Interference-Aware K-Step Reachable Communication in Multi-Agent Reinforcement Learning
This addresses communication challenges in MARL for collaborative tasks, representing an incremental improvement with specific gains.
The paper tackles the problem of limited communication bandwidth and dynamic topologies in multi-agent reinforcement learning by proposing IA-KRC, a framework that enhances cooperation through reachability protocols and interference prediction, achieving superior performance and robustness compared to state-of-the-art baselines.
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.