safe-policy-learning

本文介绍了安全对比强化学习（Safe-CRL），以解决失败终止条件下目标条件策略学习中的高估偏差问题，利用一位失败信号来提升机器人导航和运动任务的安全性和性能。

This paper introduces Opal (Opportunity-aware Policy Authorization for Laboratories), a framework that certifies whether adaptive experimentation should be enabled by precommitting to non-trivial adaptation, controlled target risk, and positive executed value after cost. It establishes an impossibility boundary and demonstrates the method on a Cell Painting dataset, achieving risk control and positive value.