arXiv (All)
open access
Action Chunking Proximal Policy Optimization with Feedback Correction
Action chunking provides temporal abstraction in reinforcement learning by selecting short action sequences instead of individual actions, but many existing approaches face two limitations in high-dimensional robotic control. First, many rely on value functions over action chunks, which can be diffi
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
- PRICE the Action Chunks: Physical Relational Credit Assignment for Embodied Reinforcement Learning#1143491
- Reinforcement Learning with Action Chunking Policies#642212
- Samples That Matter: Principled Policy Learning for Robotics#354225
- Large Reward Models: Generalizable Online Robot Reward Generation with Vision-Language Models#1155142
- SeeQ: Training Generalist Value Functions for Long-Horizon Robotic Manipulation#1036495
Record · ID 1131092
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