Unverified50% confidenceFactExact time
Theoretical 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.
1
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50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
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Related Claims
UnverifiedCompared with baselines (such as using only target data, simple data mixing, etc.), DFQI shows advantages in both policy return and stability.76% similarUnverifiedDFQI introduces an L1 penalty term in the fitted Q-iteration framework, pulling the target coefficients toward a weighted combination of the source coefficients, with weights determined adaptively from the data.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.73% similarVerifiedValue-function-based methods such as DQN indirectly derive the policy by estimating the Q value of each state-action pair, and typically need to maintain an experience replay buffer and a target network to stabilize training.72% similarUnverifiedThe effectiveness of threshold functions is highly dependent on the calibration quality of the underlying model's output probabilities, where model calibration means that predicted probabilities should genuinely reflect the actual frequency of event occurrence.71% similar
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