arXiv:2607. 05375v1 Announce Type: cross Abstract: Occupancy ratios correct distribution shift in offline reinforcement learning and are central to off-policy evaluation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with its discounted occupancy ratio, characterized by an adjoint Bellman equation. Existing mini...
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...
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:2512.23927v3 Announce Type: replace-cross Abstract: Fitted \(Q\)-iteration (FQI) is a standard regression-based method for optimal control in offline reinforcement learning, but its stability u...
arXiv:2608. 14401v1 Announce Type: cross Abstract: In offline RL, estimating the optimal action-value function $Q^*$ can be formulated as solving the optimal Bellman equation based solely on offline observations.