Transferred Q-learning

• URL: http://arxiv.org/abs/2202.04709v1
• Date: Wed, 9 Feb 2022 20:08:19 GMT
• Title: Transferred Q-learning
• Authors: Elynn Y. Chen, Michael I. Jordan, Sai Li
• Abstract summary: We consider $Q$-learning with knowledge transfer, using samples from a target reinforcement learning (RL) task as well as source samples from different but related RL tasks. We propose transfer learning algorithms for both batch and online $Q$-learning with offline source studies.
• Score: 79.79659145328856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider $Q$-learning with knowledge transfer, using samples from a target reinforcement learning (RL) task as well as source samples from different but related RL tasks. We propose transfer learning algorithms for both batch and online $Q$-learning with offline source studies. The proposed transferred $Q$-learning algorithm contains a novel re-targeting step that enables vertical information-cascading along multiple steps in an RL task, besides the usual horizontal information-gathering as transfer learning (TL) for supervised learning. We establish the first theoretical justifications of TL in RL tasks by showing a faster rate of convergence of the $Q$ function estimation in the offline RL transfer, and a lower regret bound in the offline-to-online RL transfer under certain similarity assumptions. Empirical evidences from both synthetic and real datasets are presented to back up the proposed algorithm and our theoretical results.

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