強化学習コンポーネントの安全性保証：AMLAS-RL
Assuring the Safety of Reinforcement Learning Components: AMLAS-RL
強化学習を用いたシステムの安全性を保証するため、教師あり学習向けのAMLAS手法を拡張したAMLAS-RLフレームワークを提案し、衝突回避を目指す車両の例で実証した。
詳しい要約
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著者: Calum Corrie Imrie, Ioannis Stefanakos, Sepeedeh Shahbeigi, Richard Hawkins, Simon Burton
分類: cs.LG, cs.AI, cs.RO, cs.SE
原文アブストラクト
The rapid advancement of machine learning (ML) has led to its increasing integration into cyber-physical systems (CPS) across diverse domains. While CPS offer powerful capabilities, incorporating ML components introduces significant safety and assurance challenges. Among ML techniques, reinforcement learning (RL) is particularly suited for CPS due to its capacity to handle complex, dynamic environments where explicit models of interaction between system and environment are unavailable or difficult to construct. However, in safety-critical applications, this learning process must not only be effective but demonstrably safe. Safe-RL methods aim to address this by incorporating safety constraints during learning, yet they fall short in providing systematic assurance across the RL lifecycle. The AMLAS methodology offers structured guidance for assuring the safety of supervised learning components, but it does not directly apply to the unique challenges posed by RL. In this paper, we adapt AMLAS to provide a framework for generating assurance arguments for an RL-enabled system through an iterative process; AMLAS-RL. We demonstrate AMLAS-RL using a running example of a wheeled vehicle tasked with reaching a target goal without collision.
