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Intelligent Model Learning Based on Variance for Bayesian Reinforcement Learning.

, , , , and . ICTAI, page 170-177. IEEE Computer Society, (2015)

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Trajectory Sampling Value Iteration: Improved Dyna Search for MDPs., , , and . AAMAS, page 1685-1686. ACM, (2015)Intelligent Model Learning Based on Variance for Bayesian Reinforcement Learning., , , , and . ICTAI, page 170-177. IEEE Computer Society, (2015)Covering Number for Efficient Heuristic-based POMDP Planning., , and . ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 28-36. JMLR.org, (2014)Reasoning and predicting POMDP planning complexity via covering numbers., , , and . Frontiers of Computer Science, 10 (4): 726-740 (2016)Covering Number as a Complexity Measure for POMDP Planning and Learning., , and . AAAI, AAAI Press, (2012)ACGAIL: Imitation Learning About Multiple Intentions with Auxiliary Classifier GANs., and . PRICAI (1), volume 11012 of Lecture Notes in Computer Science, page 321-334. Springer, (2018)Thompson Sampling Based Monte-Carlo Planning in POMDPs., , , and . ICAPS, AAAI, (2014)Efficient reinforcement learning in continuous state and action spaces with Dyna and policy approximation., , , and . Frontiers Comput. Sci., 13 (1): 106-126 (2019)Deep Recurrent Policy Networks for Planning Under Partial Observability., and . ICANN (1), volume 11727 of Lecture Notes in Computer Science, page 598-610. Springer, (2019)PLEASE: Palm Leaf Search for POMDPs with Large Observation Spaces., , , , and . ICAPS, page 249-258. AAAI Press, (2015)