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

2026-03-16

AI Summary

This work addresses the challenge of efficient partner selection under limited communication bandwidth and dynamically complex topologies in multi-agent reinforcement learning. To this end, the authors propose the IA-KRC framework, which uniquely integrates a K-step reachability communication protocol with an interference-aware partner selection mechanism. The former restricts message propagation to physically reachable neighbors within K hops, while the latter enhances communication decisions by predicting potential interference. This joint approach significantly improves collaborative efficiency, robustness, and scalability in highly dynamic and topologically complex environments. Empirical evaluations across multiple benchmark scenarios demonstrate that IA-KRC consistently outperforms current state-of-the-art methods.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Multi-Robot Systems
• Machine Learning: Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• multi-agent reinforcement learning
• communication
• interference
• reachability
• collaboration

Innovation

• K-Step Reachability
• Interference-Aware Communication
• Multi-Agent Reinforcement Learning
• Communication Topology
• Partner Selection