Conservative Safety Critics for Exploration
Abstract
Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exploration in RL by learning a conservative safety estimate of environment states through a critic, and provably upper bound the likelihood of catastrophic failures at every training iteration. We theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are likely to be satisfied with high probability during training, derive provable convergence guarantees for our approach, which is no worse asymptotically than standard RL, and demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks. Empirically, we show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates during training than prior methods. Videos are at this url https://sites.google.com/view/conservative-safety-critics/home
Cite
@article{arxiv.2010.14497,
title = {Conservative Safety Critics for Exploration},
author = {Homanga Bharadhwaj and Aviral Kumar and Nicholas Rhinehart and Sergey Levine and Florian Shkurti and Animesh Garg},
journal= {arXiv preprint arXiv:2010.14497},
year = {2021}
}
Comments
Published as a conference paper in ICLR 2021