Learning to solve Markovian decision processes
Learning to solve Markovian decision processes
Planning and acting in partially observable stochastic domains
Artificial Intelligence
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Applications of best-first heuristic search to finite-horizon partially observed markov decision processes
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Region-based incremental pruning for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Point-based dynamic programming for DEC-POMDPs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Efficient maximization in solving POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Speeding up the convergence of value iteration in partially observable Markov decision processes
Journal of Artificial Intelligence Research
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic robot navigation in partially observable environments
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Planning with partially observable Markov decision processes: advances in exact solution method
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
An optimal best-first search algorithm for solving infinite horizon DEC-POMDPs
ECML'05 Proceedings of the 16th European conference on Machine Learning
Cosine Policy Iteration for Solving Infinite-Horizon Markov Decision Processes
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
IEEE Transactions on Wireless Communications
Point-based policy generation for decentralized POMDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs
Autonomous Agents and Multi-Agent Systems
Online planning for multi-agent systems with bounded communication
Artificial Intelligence
Toward error-bounded algorithms for infinite-horizon DEC-POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Modeling agents and agent systems
Transactions on computational collective intelligence V
Influence diagrams with memory states: representation and algorithms
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains
Artificial Intelligence
Solving decentralized POMDP problems using genetic algorithms
Autonomous Agents and Multi-Agent Systems
Producing efficient error-bounded solutions for transition independent decentralized mdps
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
Optimally solving dec-POMDPs as continuous-state MDPs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Monte-Carlo expectation maximization for decentralized POMDPs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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 policy iteration algorithm for solving DEC-POMDPs. The algorithm uses stochastic finite-state controllers to represent policies. The solution can include a correlation device, which allows agents to correlate their actions without communicating. This approach alternates between expanding the controller and performing value-preserving transformations, which modify the controller without sacrificing value. We present two efficient value-preserving transformations: one can reduce the size of the controller and the other can improve its value while keeping the size fixed. Empirical results demonstrate the usefulness of value-preserving transformations in increasing value while keeping controller size to a minimum. To broaden the applicability of the approach, we also present a heuristic version of the policy iteration algorithm, which sacrifices convergence to optimality. This algorithm further reduces the size of the controllers at each step by assuming that probability distributions over the other agents' actions are known. While this assumption may not hold in general, it helps produce higher quality solutions in our test problems.