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 →
- Bellman completeness
- Bellman recursion
- Fitted Occupancy-Ratio Evaluation
- Kullback--Leibler (KL) divergence
- offline reinforcement learning
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