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arXiv:2608.24858v1 Announce Type: new Abstract: Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with its discounted occupancy ratio, characteri...
Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation. Existing primal-dual and minimax methods typically estimate these ratios by enforcing occupancy-balance moments over a critic class.
arXiv:2607. 05375v1 Announce Type: cross Abstract: Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation.
The paper introduces occupancy-weighted Fitted Q-Evaluation (FQE), a regression-based off‑policy evaluation method that replaces the standard offline distribution weights with a target‑policy discounted occupancy ratio. This weighting aligns the projection norm with the target policy’s dynamics, restoring contraction of the Bellman operator and eliminating the need for Bellman completeness. The authors provide finite‑sample guarantees that separate iteration, statistical, approximation, and ratio‑estimation errors, and show that exact occupancy weighting combined with fitted occupancy‑ratio evaluation yields consistent estimation under coverage without requiring critic‑side completeness.
arXiv:2501.06926v5 Announce Type: replace Abstract: Double reinforcement learning (DRL) provides efficient off-policy inference for policy values in nonparametric Markov decision processes (MDPs), bu...
arXiv:2608. 03108v1 Announce Type: new Abstract: Offline reinforcement learning (offline RL) can benefit from nearby out-of-distribution (OOD) actions, but estimation errors at these actions may be amplified by bootstrapping.