The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
Constraint-based dynamic programming for decentralized POMDPs with structured interactions
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Event-detecting multi-agent MDPs: complexity and constant-factor approximation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Decentralized POMDPs provide a rigorous framework for multi-agent decision-theoretic planning. However, their high complexity has limited scalability. In this work, we present a promising new class of algorithms based on probabilistic inference for infinite-horizon ND-POMDPs---a restricted Dec-POMDP model. We first transform the policy optimization problem to that of likelihood maximization in a mixture of dynamic Bayes nets (DBNs). We then develop the Expectation-Maximization (EM) algorithm for maximizing the likelihood in this representation. The EM algorithm for ND-POMDPs lends itself naturally to a simple message-passing paradigm guided by the agent interaction graph. It is thus highly scalable w.r.t. the number of agents, can be easily parallelized, and produces good quality solutions.