Cooperative Behavior Learning Based on Social Interaction of State Conversion and Reward Exchange Among Multi-Agents

In multi-agent systems, it is necessary for autonomous agents to interact with each other in order to have excellent cooperative performance. Therefore, we have studied social interaction between agents to see how they acquire cooperative behavior. We have found that sharing environmental states can improve agent cooperation through reinforcement learning, and that changing environmental states to target-related individual states improves cooperation. To further improve cooperation, we propose reward redistribution based on reward exchanges among agents. In receiving rewards from both the environment and other agents, agents learned how to adjust themselves to the environment and how to explore and strengthen cooperation in tasks that a single agent could not do alone. Agents thus cooperate best through the interaction of state conversion and reward exchange.