Unverified50% confidenceFactExact time
The authors establish statistical guarantees for DFQI's target estimation and policy learning in a stationary discounted setting, covering both homogeneous and heterogeneous transition kernels.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
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Related Claims
UnverifiedTheoretical 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.73% similarUnverifiedThe paper proposes Direct Transfer Fitted Q-Iteration (DFQI), which directly incorporates source-environment information into target value-function learning through a joint estimation framework.72% similarUnverifiedCompared with baselines (such as using only target data, simple data mixing, etc.), DFQI shows advantages in both policy return and stability.72% similarUnverifiedIn multiple simulated environments, when target data are limited, DFQI significantly improves policy learning performance by transferring source information.71% similarUnverifiedThe paper introduces a design efficiency factor D* to summarize the way leverage is distributed among observations, enabling the new formula to capture structural differences ignored by traditional methods.69% similar
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