Timezone: »
Provably Efficient Multi-Task Reinforcement Learning with Model Transfer
Chicheng Zhang · Zhi Wang
We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes(MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player information sharing. We design and analyze a model-based algorithm, and provide gap-dependent and gap-independent upper and lower bounds that characterize the intrinsic complexity of the problem.
Author Information
Chicheng Zhang (University of Arizona)
Zhi Wang (University of California, San Diego)
More from the Same Authors
-
2021 : Margin-distancing for safe model explanation »
Tom Yan · Chicheng Zhang -
2022 Poster: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri -
2022 Poster: Active fairness auditing »
Tom Yan · Chicheng Zhang -
2022 Spotlight: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri -
2022 Oral: Active fairness auditing »
Tom Yan · Chicheng Zhang -
2020 : Invited Talk 6 Q&A - Chicheng Zhang »
Chicheng Zhang -
2020 : Invited Talk 6 - Efficient continuous-action contextual bandits via reduction to extreme multiclass classification - Chicheng Zhang »
Chicheng Zhang