Distorted Distributional Policy Evaluation for Offline Reinforcement Learning

2026-01-05

arXiv.org

AI Summary

This work addresses the over-conservatism inherent in existing offline distributional reinforcement learning methods, which typically employ a uniform pessimism strategy to estimate return quantiles, thereby impairing generalization. To overcome this limitation, the authors propose a quantile distortion mechanism that dynamically adjusts the degree of pessimism at each quantile based on data support, enabling non-uniformly pessimistic distributional policy evaluation. By integrating quantile regression with a theoretically grounded distortion function, the approach relaxes the conventional assumption of uniform pessimism. Empirical evaluations across multiple benchmark tasks demonstrate significant performance improvements over state-of-the-art methods, validating the effectiveness of non-uniform pessimism in enhancing both algorithmic adaptability and value estimation accuracy.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Distributed Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

While Distributional Reinforcement Learning (DRL) methods have demonstrated strong performance in online settings, its success in offline scenarios remains limited. We hypothesize that a key limitation of existing offline DRL methods lies in their approach to uniformly underestimate return quantiles. This uniform pessimism can lead to overly conservative value estimates, ultimately hindering generalization and performance. To address this, we introduce a novel concept called quantile distortion, which enables non-uniform pessimism by adjusting the degree of conservatism based on the availability of supporting data. Our approach is grounded in theoretical analysis and empirically validated, demonstrating improved performance over uniform pessimism.

Problem

• Offline Reinforcement Learning
• Distributional Reinforcement Learning
• Quantile Underestimation
• Pessimism
• Return Distribution

Innovation

• quantile distortion
• offline reinforcement learning
• distributional reinforcement learning
• non-uniform pessimism
• policy evaluation