Efficient Communication With Skill Neurons in Decentralized Multi-Agent Reinforcement Learning.

Abstract

Multi-agent reinforcement learning (MARL) is a powerful paradigm for large-scale collaborative scenarios, yet it is often hampered by partial observability and non-stationarity. While communication can alleviate these issues, designing efficient protocols remains a significant challenge, especially in decentralized settings. Many existing methods suffer from high communication volume and training complexity. To overcome these limitations, we propose CSN, which enables efficient Communication with Skill Neurons in decentralized MARL by exchanging the essential components of learned knowledge at neuron level. Specifically, we first identify skill neurons, a subset of neurons that encode the most critical knowledge acquired during local training. By communicating only a sparse subset of model parameters and doing so intermittently, CSN fundamentally reduces communication volume by avoiding redundant information and frequent communication. Extensive experiments on the SMAC, SMACv2, MPE and Predator-Prey benchmarks validate that CSN significantly outperforms state-of-the-art methods in both performance and communication efficiency, verifying its effectiveness in decentralized MARL.