Policy Iteration for Decentralized Control of Markov Decision Processes

  title={Policy Iteration for Decentralized Control of Markov Decision Processes},
  author={Daniel S. Bernstein and Christopher Amato and Eric A. Hansen and Shlomo Zilberstein},
  journal={J. Artif. Intell. Res.},
Coordination of distributed agents is required for problems arising in many areas, including multi-robot systems, networking and e-commerce. As a formal framework for such problems, we use the decentralized partially observable Markov decision process (DECPOMDP). Though much work has been done on optimal dynamic programming algorithms for the single-agent version of the problem, optimal algorithms for the multiagent case have been elusive. The main contribution of this paper is an optimal… CONTINUE READING
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