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Evidence record 16106 · automatically gathered

Diffusion Policy with Behavioral Advantage Correction for Offline Reinforcement Learning

In offline reinforcement learning (RL), the distribution shift between behavioral data and the learned policy can lead to erroneous \emph{Q}-value estimation, thereby misguiding the direction of policy optimization. To address this issue, we develop a behavioral advantage corrected policy evaluation (BAC-PE) approach, which utilizes the \emph{Q}-function of the behavior policy to correct the learned policy's \emph{Q}-function, thus mitigating pessimistic conservatism and overestimation bias. Fur

Record details

Published: 3 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Bias & fairness · Regulation
Retrieved: 4 August 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (3 August 2026), “Diffusion Policy with Behavioral Advantage Correction for Offline Reinforcement Learning,” evidence record 16106, https://ethics.ai/record/16106 (originally published by arXiv cs.AI).

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