Unverified50% confidenceFactExact time
For heterogeneous transition kernels, DFQI automatically adjusts the contribution of source information through weighted aggregation, avoiding negative transfer.
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50%
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Long-term
Relevance
11/3/2026
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Related Claims
UnverifiedIn each Bellman update, DFQI simultaneously updates the target Q-function and borrows source information, achieving tighter coupling.72% 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.68% similarUnverifiedIn multiple simulated environments, when target data are limited, DFQI significantly improves policy learning performance by transferring source information.65% similarUnverifiedCompared with baselines (such as using only target data, simple data mixing, etc.), DFQI shows advantages in both policy return and stability.64% 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.63% similar
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