Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing

Yuxuan Xie, Jilles Dibangoye, Olivier Buffet
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10473-10482, 2020.

Abstract

Optimally solving decentralized partially observable Markov decision processes under either full or no information sharing received significant attention in recent years. However, little is known about how partial information sharing affects existing theory and algorithms. This paper addresses this question for a team of two agents, with one-sided information sharing—\ie both agents have imperfect information about the state of the world, but only one has access to what the other sees and does. From the perspective of a central planner, we show that the original problem can be reformulated into an equivalent information-state Markov decision process and solved as such. Besides, we prove that the optimal value function exhibits a specific form of uniform continuity. We also present a heuristic search algorithm utilizing this property and providing the first results for this family of problems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-xie20a, title = {Optimally Solving Two-Agent Decentralized {POMDP}s Under One-Sided Information Sharing}, author = {Xie, Yuxuan and Dibangoye, Jilles and Buffet, Olivier}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {10473--10482}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/xie20a/xie20a.pdf}, url = {https://proceedings.mlr.press/v119/xie20a.html}, abstract = {Optimally solving decentralized partially observable Markov decision processes under either full or no information sharing received significant attention in recent years. However, little is known about how partial information sharing affects existing theory and algorithms. This paper addresses this question for a team of two agents, with one-sided information sharing—\ie both agents have imperfect information about the state of the world, but only one has access to what the other sees and does. From the perspective of a central planner, we show that the original problem can be reformulated into an equivalent information-state Markov decision process and solved as such. Besides, we prove that the optimal value function exhibits a specific form of uniform continuity. We also present a heuristic search algorithm utilizing this property and providing the first results for this family of problems.} }
Endnote
%0 Conference Paper %T Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing %A Yuxuan Xie %A Jilles Dibangoye %A Olivier Buffet %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-xie20a %I PMLR %P 10473--10482 %U https://proceedings.mlr.press/v119/xie20a.html %V 119 %X Optimally solving decentralized partially observable Markov decision processes under either full or no information sharing received significant attention in recent years. However, little is known about how partial information sharing affects existing theory and algorithms. This paper addresses this question for a team of two agents, with one-sided information sharing—\ie both agents have imperfect information about the state of the world, but only one has access to what the other sees and does. From the perspective of a central planner, we show that the original problem can be reformulated into an equivalent information-state Markov decision process and solved as such. Besides, we prove that the optimal value function exhibits a specific form of uniform continuity. We also present a heuristic search algorithm utilizing this property and providing the first results for this family of problems.
APA
Xie, Y., Dibangoye, J. & Buffet, O.. (2020). Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:10473-10482 Available from https://proceedings.mlr.press/v119/xie20a.html.

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