L1適応制御による強化学習ポリシーのロバスト性向上
Improving the Robustness of Reinforcement Learning Policies with $\mathcal{L}_{1}$ Adaptive Control
訓練済みの強化学習ポリシーにL1適応制御器を追加し、環境の動的変動に対するロバスト性を高める手法を提案した論文。
著者: Y. Cheng, P. Zhao, F. Wang, D. J. Block, N. Hovakimyan
分類: cs.RO
原文アブストラクト
A reinforcement learning (RL) control policy could fail in a new/perturbed environment that is different from the training environment, due to the presence of dynamic variations. For controlling systems with continuous state and action spaces, we propose an add-on approach to robustifying a pre-trained RL policy by augmenting it with an $\mathcal{L}_{1}$ adaptive controller ($\mathcal{L}_{1}$AC). Leveraging the capability of an $\mathcal{L}_{1}$AC for fast estimation and active compensation of dynamic variations, the proposed approach can improve the robustness of an RL policy which is trained either in a simulator or in the real world without consideration of a broad class of dynamic variations. Numerical and real-world experiments empirically demonstrate the efficacy of the proposed approach in robustifying RL policies trained using both model-free and model-based methods.