Visual Explanation for Cooperative Behavior in Multi-Agent Reinforcement Learning
- Author
- Hidenori Itaya, Tom Sagawa, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
- Publication
- The International Joint Conference on Neural Networks, 2023
Download: PDF (English)
Multi-agent reinforcement learning (MARL) can acquire cooperative behavior among agents by training multiple agents in the same environment. Therefore, it is expected to be applied to complex tasks in real environments, such as traffic signal control in a traffic environment and cooperative behavior of robots. In this study, using the multi-actor-attention-critic (MAAC) with the actor-critic method as a basis, we introduce an attention head for the actor that calculates the agent’s action. In contrast to the critic in MAAC, which shares the attention head among all the agents, the attention head of the actor in our method is constructed independently for each agent. This allows the attention head of the actor to calculate actor-attention (indicating which other agents are gazed at by each agent) and to acquire cooperative behavior. We visualize actor-attention to analyze the basis of agents’ decisions for cooperative behavior. Using single spread, which is a multi-agent environment for cooperative problems, we show that the basis of decisions for cooperative behavior can be easily analyzed. We also demonstrate that our method efficiently obtains cooperative behavior considering other agents through quantitative evaluation of the cooperative behavior.