Improving PAC exploration using the median of means
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We present the first application of the median of means in a PAC exploration algorithm for MDPs. Using the median of means allows us to significantly reduce the dependence of our bounds on the range of values that the value function can take, while introducing a dependence on the (potentially much smaller) variance of the Bellman operator. Additionally, our algorithm is the first algorithm with PAC bounds that can be applied to MDPs with unbounded rewards.
Date issued
2016Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Laboratory for Information and Decision SystemsJournal
Advances in Neural Information Processing Systems (NIPS)
Publisher
Neural Information Processing Systems Foundation
Citation
Pazis, Jason et al. "Improving PAC Exploration Using the Median Of Means." Advances in Neural Information Processing Systems (NIPS 2016), 29 (2016) © 2016 NIPS Foundation - All Rights Reserved.
Version: Final published version
ISSN
1049-5258