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This work develops a fully decentralized multi-agent algorithm for policyevaluation. The proposed scheme can be applied to two distinct scenarios. Inthe first scenario, a collection of agents have distinct datasets gatheredfollowing different behavior policies (none of which is required to explore thefull state space) in different instances of the same environment and they allcollaborate to evaluate a common target policy. The network approach allows forefficient exploration of the state space and allows all agents to converge tothe optimal solution even in situations where neither agent can converge on itsown without cooperation. The second scenario is that of multi-agent games, inwhich the state is global and rewards are local. In this scenario, agentscollaborate to estimate the value function of a target team policy. Theproposed algorithm combines off-policy learning, eligibility traces and linearfunction approximation. The proposed algorithm is of the variance-reduced kindand achieves linear convergence with $O(1)$ memory requirements. The linearconvergence of the algorithm is established analytically, and simulations areused to illustrate the effectiveness of the method.

Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates