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A Dynamic Programming Algorithm for Decentralized Markov Decision Processes With a Broadcast Structure

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Executive Summary

The authors give an optimal dynamic programming algorithm to solve a class of finite-horizon decentralized Markov Decision Processes (MDPs). They consider problems with a broadcast information structure that consists of a central node that only has access to its own state but can affect several outer nodes, while each outer node has access to both its own state and the central node's state, but cannot affect the other nodes. The solution to this problem involves a dynamic program similar to that of a centralized partially-observed Markov decision process.

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