状態制約付き安全強化学習のサーベイ
State-wise Safe Reinforcement Learning: A Survey
状態ごとの制約を課す安全強化学習の既存手法を、安全性保証・拡張性・報酬性能・収束前後の安全性の観点から整理し、課題と今後の方向性を論じたサーベイ論文。
著者: Weiye Zhao, Tairan He, Rui Chen, Tianhao Wei, Changliu Liu
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major concern is safety, in another word, constraint satisfaction. State-wise constraints are one of the most common constraints in real-world applications and one of the most challenging constraints in Safe RL. Enforcing state-wise constraints is necessary and essential to many challenging tasks such as autonomous driving, robot manipulation. This paper provides a comprehensive review of existing approaches that address state-wise constraints in RL. Under the framework of State-wise Constrained Markov Decision Process (SCMDP), we will discuss the connections, differences, and trade-offs of existing approaches in terms of (i) safety guarantee and scalability, (ii) safety and reward performance, and (iii) safety after convergence and during training. We also summarize limitations of current methods and discuss potential future directions.
