The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Decentralized Markov Decision Processes with Event-Driven Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
An analytic solution to discrete Bayesian reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
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Forming commitments about abstract influences that agents can exert on one another has shown promise in improving the tractability of multiagent coordination under uncertainty. We now extend this approach to domains with meta-level reward-model uncertainty. Intuitively, an agent may actually improve collective performance by forming a weaker commitment that allows more latitude to adapt its policy as it refines its reward model. To account for reward uncertainty as such, we introduce and contrast three new techniques.