DFQI transfers knowledge for offline Q-learning
The method integrates source data via L1 penalty in target Q-function updates to improve policy learning with limited target data in marketing and healthcare.
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Relatedcausal-inference1confounding1distribution-shift1domain-shift1fitted-iteration1knowledge-transfer1latent-divergence1policy-optimization1policy-regularization1q-learning1
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Stories tagged #offline-reinforcement-learning
Offline RL method handles domain shift and unmeasured confounding
CAT-CCB and CAT-CMDP use causal transportability and pessimistic optimization, with finite-sample suboptimality bounds.
Offline RL paper regularizes policies via consequence distributions
Structural Behavior Regularization replaces an intractable dynamics constraint with a closed-form latent divergence and reports stronger offline RL benchmark results.