Multi-agent reinforcement learning with cooperation based on eligibility traces

The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent's be-havior usually affects the others' behaviors. In traditional reinforcement learning, one agent takes the others lo-cation, so it is difficult to consider the others' behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent esti-mates the other agent's behavior with the other agent's eligibility traces. The results of this simulation prove the validity of the proposed learning method.