Autonomous Learning of Reward Distribution for Each Agent in Multi-Agent Reinforcement Learning

A novel approach for the reward distribution in multi-agent reinforcement learning is proposed. The agent who gets a reward gives a part of it to the other agents. If an agent gives a part of its own reward to the other ones, they may help the agent to get more reward. There may be some cases in which the agent gets more reward than that it gave to the other ones. In this case, it is better for the agent to give the part of the reward to the other ones. Based on this principle, each agent learns the reward distribution ratio to the other agents autonomously based on the selfish value function. Some simulations have been demonstrated that a rational reward distribution ratio is obtained by each agent depending on the given task.