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New FORE method improves offline reinforcement learning evaluation

By PulseAugur Editorial · [3 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. FORE's key innovation is its reduced approximation condition, requiring only the realizability of the discounted occupancy ratio itself, rather than more complex conditions like Bellman completeness. This approach enables direct value estimation and doubly robust estimation, offering a more robust method for offline policy evaluation. AI

IMPACT Introduces a more robust method for offline reinforcement learning evaluation by relaxing strict mathematical conditions.

RANK_REASON The cluster contains an academic paper detailing a new method for reinforcement learning.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New FORE method improves offline reinforcement learning evaluation

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    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…

  2. 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 …

  3. 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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