Direct Knowledge Transfer for Offline Q-Learning

Abstract:Data-driven sequential decision making in marketing and healthcare can benefit from observations collected in related environments, particularly when data in the environment of interest are limited. Transferring such information requires accounting for differences in rewards and transition dynamics across environments. We study knowledge transfer for offline reinforcement learning through a joint estimation framework that directly integrates source information into target value-function learning. We propose Direct Transfer Fitted Q-Iteration (DFQI), which updates the target Q-function while simultaneously borrowing the source information through an L1 penalty that links the target coefficients to a weighted aggregate of source coefficients, with both determined jointly within each Bellman update. Using sieve approximation, we establish statistical guarantees for target estimation and policy learning in stationary discounted environments with common or heterogeneous transition kernels. Empirical studies further demonstrate the effectiveness of the proposed DFQI framework in transferring source information to improve policy learning with limited target data.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2611.01942 [stat.ML]

(or arXiv:2611.01942v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2611.01942 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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