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End-to-End Reinforcement Learning* — DOI: <https://doi.org/10.1177/02783649251401926> ·
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Project page: <https://michaelfyang.github.io/sru-project-website/>
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### Related Work
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<!-- Add release notes and updates here -->
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---
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## 2. Key Features
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> (`policy_scale`, control frequency, topic names, ONNX tensor shapes) — those are the ones
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> that must match for correct behavior.
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> The quality of code generated via the prompt workflow may vary depending on
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> the capability of the LLM used. In some cases, the generated code may require further
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> iteration and refinement with the LLM to achieve full functionality. Always validate
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> against the `Deployment/` reference and run the acceptance checks before deploying to
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> real hardware.
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---
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## 6. Training Guide
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