Attention based Reinforcement Learning for Efficient Communication under Constraint in Multi-Agent Systems
Jianguo Mei, Zhibin Quan, Wankou Yang, Xianghui Cao · 2023
Efficient communication is a key element of team collaboration in the real-world, and in multi-agent reinforcement learning (MARL) as well. When to communicate with whom and what is the main issue in MARL communication. We propose Graph-attention-Actor Attention-Critic (GA3C) on the basis of actor-critic in which we use graph generator consist of a graph attention networks (GAT) Encoder and multi-layer perceptron (MLP) Decoder to construct directed communication graph determining when to communicate with whom, based on which message processor quickly aggregates and processes messages from agents to achieve communication process. During the training process, critic with attention mechanism makes a more reasonable evaluation of the actor's strategy by dynamically selecting the agents that need attention. Empirical evaluations on the predator-prey environment demonstrate that our method has high-performance compared to other baselines, while having better scalability in more complex environments.