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Most works on multi-agent reinforcement learning focus on scenarios where thestate of the environment is fully observable. In this work, we consider acooperative policy evaluation task in which agents are not assumed to observethe environment state directly. Instead, agents can only have access to noisyobservations and to belief vectors. It is well-known that finding globalposterior distributions under multi-agent settings is generally NP-hard. As aremedy, we propose a fully decentralized belief forming strategy that relies onindividual updates and on localized interactions over a communication network.In addition to the exchange of the beliefs, agents exploit the communicationnetwork by exchanging value function parameter estimates as well. Weanalytically show that the proposed strategy allows information to diffuse overthe network, which in turn allows the agents' parameters to have a boundeddifference with a centralized baseline. A multi-sensor target trackingapplication is considered in the simulations.

Policy Evaluation in Decentralized POMDPs with Belief Sharing