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Evidence record 4412 · automatically gathered

Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning

Cross-domain offline reinforcement learning aims to adapt a policy from a source domain to a target domain using only pre-collected datasets, where environment dynamics may differ. A key challenge is to leverage source data while reducing distributional mismatch, particularly when the target dataset is extremely limited. To address this, we propose Target-aligned Coverage Expansion (TCE), a framework that decides how source data should be used, either by directly incorporating target-near transi

Record details

Published: 13 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Environment
Retrieved: 14 July 2026

source-onlyevidence status

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.

Shared topic: Regulation · Shared topic: Environment

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Shared topic: Regulation · Shared topic: Environment

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How to cite this record

ethics.ai (13 May 2026), “Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning,” evidence record 4412, https://ethics.ai/record/4412 (originally published by arXiv).

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