バリア証明付き適応強化学習とブラシボットナビゲーションへの応用
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation
非定常なダイナミクスを持つシステムに対し、適応モデル学習と制御バリア証明を組み合わせて安全性を保証する強化学習フレームワークを提案し、実機ブラシボットで検証した。
著者: Motoya Ohnishi, Li Wang, Gennaro Notomista, Magnus Egerstedt
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates which constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under stationarity assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex and nonstationary.
