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Reinforcement Learning Algorithms for Homogenous Multi-Agent Systems

We examine the situation in which multiple reinforcement learning agents attempt to speed up the learning process by sharing information. The agents do not interact with each other in the enviroment; instead each agent is learning the same task in isolation from the other agents. As reinforcement learning is a trial-and-error approach, an agent’s performance in the environment typically increases with the level of experience an agent has in interacting with that environment. We show how the agents can share Q-values in order to arrive at an optimal policy using fewer trials.