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We apply diffusion strategies to develop a fully-distributed cooperativereinforcement learning algorithm in which agents in a network communicate onlywith their immediate neighbors to improve predictions about their environment.The algorithm can also be applied to off-policy learning, meaning that theagents can predict the response to a behavior different from the actualpolicies they are following. The proposed distributed strategy is efficient,with linear complexity in both computation time and memory footprint. Weprovide a mean-square-error performance analysis and establish convergenceunder constant step-size updates, which endow the network with continuouslearning capabilities. The results show a clear gain from cooperation: when theindividual agents can estimate the solution, cooperation increases stabilityand reduces bias and variance of the prediction error; but, more importantly,the network is able to approach the optimal solution even when none of theindividual agents can (e.g., when the individual behavior policies restricteach agent to sample a small portion of the state space).

Distributed Policy Evaluation Under Multiple Behavior Strategies