  We consider the problem of interactive partially observable Markov decision
processes (I-POMDPs), where the agents are located at the nodes of a
communication network. Specifically, we assume a certain message type for all
messages. Moreover, each agent makes individual decisions based on the
interactive belief states, the information observed locally and the messages
received from its neighbors over the network. Within this setting, the
collective goal of the agents is to maximize the globally averaged return over
the network through exchanging information with their neighbors. We propose a
decentralized belief propagation algorithm for the problem, and prove the
convergence of our algorithm. Finally we show multiple applications of our
framework. Our work appears to be the first study of decentralized belief
propagation algorithm for networked multi-agent I-POMDPs.
