arXiv (All)
open access
Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance
Online reinforcement learning (RL) enables driving policies to adapt to changing deployment conditions, but requires safe exploration throughout training. Safety filtering alone does not ensure stable policy improvement or reduced dependence on corrections. We propose Safe Online Adaptive Refinement
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
- Demonstration-Guided Observation Attacks on Black-Box Safe Reinforcement Learning Controllers for Robotic Systems#1187319
- SafePG: Safe and Globally Optimal Reinforcement Learning with Hard Constraints#1083552
- FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systems#792921
- FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systems#788917
- Trust the Batch, On- or Off-Policy: Adaptive Policy Optimization for RL Post-Training#178888
Record · ID 1343006
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