リアプノフ障壁関数による安全制約付き強化学習ポリシー最適化
Lyapunov Barrier Policy Optimization
リアプノフ障壁関数を用いてポリシー更新を安全集合内に制限し、学習中の制約違反を大幅に削減する強化学習手法LBPOを提案した。
著者: Harshit Sikchi, Wenxuan Zhou, David Held
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Deploying Reinforcement Learning (RL) agents in the real-world require that the agents satisfy safety constraints. Current RL agents explore the environment without considering these constraints, which can lead to damage to the hardware or even other agents in the environment. We propose a new method, LBPO, that uses a Lyapunov-based barrier function to restrict the policy update to a safe set for each training iteration. Our method also allows the user to control the conservativeness of the agent with respect to the constraints in the environment. LBPO significantly outperforms state-of-the-art baselines in terms of the number of constraint violations during training while being competitive in terms of performance. Further, our analysis reveals that baselines like CPO and SDDPG rely mostly on backtracking to ensure safety rather than safe projection, which provides insight into why previous methods might not have effectively limit the number of constraint violations.
