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Seonvin Cho

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Articles

Computer Science > Machine Learning

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By Seonvin Cho, Songnam Hong
| arXiv
Offline reinforcement learning (RL) must reconcile two competing requirements: policy updates should stay near dataset-supported actions to keep value estimates reliable, yet meaningful gains often require moving beyond the behavior distribution. We develop a geometric view of offline actor updates by modeling policies as a probability manifold endowed with a chosen metric geometry.
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