Towards Communication Efficient Multi-Agent Cooperations: Reinforcement Learning and LLM

Multi-agent systems (MAS) have gained popularity due to their effectiveness in diverse applications. Among these, decentralized approaches, which rely on inter-agent communication, have emerged as a promising alternative to centralized MAS coordination methods. However, existing communication protocols often learn abstract representations that are difficult for humans to interpret. Also, the limited wireless bandwidth in real-world applications imposes a significant challenge on efficient communication among agents. To address these issues, in this paper, we consider a multi-robot navigation scenario and propose a goal-oriented human-interpretable feature communication protocol, enabling robots to transmit only the most semantically important features. Building on this protocol, we first introduce a Multi-Agent Proximal Policy Optimization (MAPPO)-based Bandwidth-Adaptive Dual-Policy (MAPPO BDP) framework, which employs a dual-policy architecture to jointly optimize feature selection and navigation under bandwidth constraints. However, as MAPPO-BDP requires retraining for different bandwidth constraints, we further propose a Large Language Model (LLM)-based Bandwidth-Adaptive Dual-Policy (LLM-BDP) framework. By leveraging LLM-based multi-agents, LLM-BDP can adapt to varying bandwidth constraints by simply modifying the input prompt, thereby eliminating the need for re training. Experimental results show that both MAPPO-BDP and LLM-BDP frameworks successfully complete navigation tasks within a limited number of steps by effectively communicating the most important features under bandwidth constraints. We demonstrate that LLM-BDP achieves better reasoning adapt ability by utilizing more advanced LLMs and engineering example prompts. Notably, with human-interpretable feature communication protocol, robots trained with the MAPPO-BDP framework can seamlessly collaborate with robots from the LLM BDP framework to complete tasks, even without co-training, demonstrating the practical applicability of the proposed communication protocol in new scenarios.