Learning Heterogeneous Strategies via Graph-based Multi-agent Reinforcement Learning
Yang Li, Xiangfeng Luo, Shaorong Xie · 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) · 2021
In a mixed cooperative-competitive environment, each agent needs to learn heterogeneous strategies. The complex game relationship between heterogeneous agents causes difficulties for strategy learning. In this paper, we propose a graph-based multi-agent reinforcement learning method to simplify the learning process, named hierarchical heterogeneous graph multi-agent actor-critic (H2G-MAAC). The method first uses a hierarchical heterogeneous graph to model the hierarchical relationships among multiple heterogeneous agents. Then, it conducts representation learning for multiple agents with hierarchical graph attention networks. Finally, it learns heterogeneous strategies with multi-agent actor-critic. We conduct experiments in Predator-Prey games. The results indicate that the proposed method can simplify the learning process and outperforms existing state-of-the-art methods.