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New FORE method simplifies offline reinforcement learning evaluation

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Fitted Occupancy-Ratio Evaluation (FORE), a novel method for estimating occupancy ratios in offline reinforcement learning. This technique characterizes the discounted occupancy ratio through an adjoint Bellman recursion, solving a density-ratio objective at each iteration. Unlike previous methods requiring Bellman completeness, FORE's core approximation condition is the realizability of the discounted occupancy ratio itself, offering convergence guarantees and finite-sample bounds. AI

IMPACT Introduces a new theoretical framework for offline reinforcement learning, potentially improving model evaluation accuracy.

RANK_REASON The cluster contains a research paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New FORE method simplifies offline reinforcement learning evaluation

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Lars van der Laan, Nathan Kallus ·

    Fitted Occupancy-Ratio Evaluation without Bellman Completeness

    arXiv:2607.05375v1 Announce Type: new Abstract: 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 …

  2. arXiv stat.ML TIER_1 English(EN) · Nathan Kallus ·

    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…

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