保守的安全クリティックによる安全な探索
Conservative Safety Critics for Exploration
クリティックで状態の安全度を保守的に見積もり、訓練中の壊滅的失敗の確率を理論的に上限付きで抑えつつ探索を可能にする安全強化学習手法を提案し、ナビゲーション・マニピュレーション・歩行タスクで有効性を示した。
著者: Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg
分類: cs.LG, cs.AI, cs.RO, stat.ML
原文アブストラクト
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
