Discrete-time controlled Markov processes with average cost criterion: a survey
SIAM Journal on Control and Optimization
Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement learning
Competitive Markov decision processes
Competitive Markov decision processes
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Complexity results for infinite-horizon markov decision processes
Complexity results for infinite-horizon markov decision processes
Mixed-integer programming methods for finding Nash equilibria
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A unification of extensive-form games and Markov decision processes
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Anytime coordination using separable bilinear programs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A bilinear programming approach for multiagent planning
Journal of Artificial Intelligence Research
Self-organization for coordinating decentralized reinforcement learning
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
An investigation into mathematical programming for finite horizon decentralized POMDPs
Journal of Artificial Intelligence Research
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Formal analysis of decentralized decision making has become a thriving research area in recent years, producing a number of multi-agent extensions of Markov decision processes. While much of the work has focused on optimizing discounted cumulative reward, optimizing average reward is sometimes a more suitable criterion. We formalize a class of such problems and analyze its characteristics, showing that it is NP complete and that optimal policies are deterministic. Our analysis lays the foundation for designing two optimal algorithms. Experimental results with a standard problem from the literature illustrate the applicability of these solution techniques.