Fitted Occupancy-Ratio Evaluation without Bellman Completeness
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. We propose fitted occupancy-ratio evaluation (FORE), a fitted fixed-point method that characterizes the discounted occupancy ratio through an adjoint Bellman recursion. At each iteration, FORE solves a single-level density-ratio objective on one-step-transition data, thereby projecting the adjoint Bellman image onto a log-ratio class in Kullback-Leibler (KL) divergence. Unlike analyses of fitted Q-evaluation, which typically require value-f...
| Authors | Lars van der Laan, Nathan Kallus |
|---|---|
| Primary category | stat.ML |
| Published | 6 July 2026 |
| Last updated at source | 28 July 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.