Unverified50% confidenceFactExact time
The paper proposes Direct Transfer Fitted Q-Iteration (DFQI), which directly incorporates source-environment information into target value-function learning through a joint estimation framework.
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11/3/2026
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UnverifiedIn multiple simulated environments, when target data are limited, DFQI significantly improves policy learning performance by transferring source information.73% similarUnverifiedThe authors establish statistical guarantees for DFQI's target estimation and policy learning in a stationary discounted setting, covering both homogeneous and heterogeneous transition kernels.72% similarUnverifiedIn each Bellman update, DFQI simultaneously updates the target Q-function and borrows source information, achieving tighter coupling.69% similarUnverifiedTheoretical analysis shows that, under certain regularity conditions, the target Q-function estimation and policy value convergence rates of DFQI are comparable to estimators that use only target data, while it can effectively exploit source data to reduce variance.68% similarUnverifiedFor heterogeneous transition kernels, DFQI automatically adjusts the contribution of source information through weighted aggregation, avoiding negative transfer.68% similar
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