arXiv (All)
open access
End-to-end QP-based policies: A unified perspective on robust control and robot learning
We present an end-to-end QP-based policy framework that enables systematic policy construction with minimal domain-specific design, while preserving the transparency and interpretability of model-based control. The proposed policy representation supports both domain-randomized model-based auto-tunin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Privileged observations enable rapid and reliable policy discovery directly in the physical world#1070736
- Advanced Policies: A First-Principles Path from Policy Gradient to Q-Learning#1117185
- SafePG: Safe and Globally Optimal Reinforcement Learning with Hard Constraints#1083552
- SUN: Persistent Programs For Language-Grounded Control-to-Learning-to-Real Policies#622553
- HiRE: Hindsight Reward Editing for Policy Finetuning#1063279
Record · ID 1115861
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive