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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)

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