転移学習による効率的な反復型安全性検証
Transfer Learning for Efficient Iterative Safety Validation
強化学習ベースの安全性検証アルゴリズムに転移学習を導入し、過去の検証タスクの知識を注意重みと状態・行動価値変換で再利用することで、関連システムの検証を効率化した研究。
著者: Anthony Corso, Mykel J. Kochenderfer
分類: cs.LG, cs.RO
原文アブストラクト
Safety validation is important during the development of safety-critical autonomous systems but can require significant computational effort. Existing algorithms often start from scratch each time the system under test changes. We apply transfer learning to improve the efficiency of reinforcement learning based safety validation algorithms when applied to related systems. Knowledge from previous safety validation tasks is encoded through the action value function and transferred to future tasks with a learned set of attention weights. Including a learned state and action value transformation for each source task can improve performance even when systems have substantially different failure modes. We conduct experiments on safety validation tasks in gridworld and autonomous driving scenarios. We show that transfer learning can improve the initial and final performance of validation algorithms and reduce the number of training steps.
