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

arXiv: https://arxiv.org/abs/2603.15054 · HTML: https://mlsanity.com/paper/2603.15054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [FeUdal Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1703.01161/) (2017)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Learning Attentional Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/1805.07733/) (2018)
- [IntentNet: Learning to Predict Intention from Raw Sensor Data](https://mlsanity.com/paper/2101.07907/) (2021)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2006.11485/) (2020)
- [Learning Multi-Agent Communication from Graph Modeling Perspective](https://mlsanity.com/paper/2405.08550/) (2024)
- [Deep Reinforcement Learning for Swarm Systems](https://mlsanity.com/paper/1807.06613/) (2018)
- [Multi-Agent Determinantal Q-Learning](https://mlsanity.com/paper/2006.01482/) (2020)
- [Learning Structured Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2002.04235/) (2020)
- [Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2010.04740/) (2020)
- [Multi-Agent Coordination via Multi-Level Communication](https://mlsanity.com/paper/2209.12713/) (2022)
- [Neurosymbolic Transformers for Multi-Agent Communication](https://mlsanity.com/paper/2101.03238/) (2021)
- [Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication](https://mlsanity.com/paper/2004.00470/) (2020)
- [Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems](https://mlsanity.com/paper/2302.05007/) (2023)
- [Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2408.07397/) (2024)
- [The Power of Communication in a Distributed Multi-Agent System](https://mlsanity.com/paper/2111.15611/) (2021)
- [Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring](https://mlsanity.com/paper/2011.01129/) (2020)

## Abstract (arXiv)

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.