ロバストで効率的なロボット強化学習のためのリアプノフ設計
Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning
制御リアプノフ関数をコストに組み込むことで、安定化制御器を少ないデータで学習できる強化学習手法を提案し、実機の台車型倒立振子と四足歩行ロボットで検証した。
著者: Tyler Westenbroek, Fernando Castaneda, Ayush Agrawal, Shankar Sastry, Koushil Sreenath
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Recent advances in the reinforcement learning (RL) literature have enabled roboticists to automatically train complex policies in simulated environments. However, due to the poor sample complexity of these methods, solving RL problems using real-world data remains a challenging problem. This paper introduces a novel cost-shaping method which aims to reduce the number of samples needed to learn a stabilizing controller. The method adds a term involving a Control Lyapunov Function (CLF) -- an `energy-like' function from the model-based control literature -- to typical cost formulations. Theoretical results demonstrate the new costs lead to stabilizing controllers when smaller discount factors are used, which is well-known to reduce sample complexity. Moreover, the addition of the CLF term `robustifies' the search for a stabilizing controller by ensuring that even highly sub-optimal polices will stabilize the system. We demonstrate our approach with two hardware examples where we learn stabilizing controllers for a cartpole and an A1 quadruped with only seconds and a few minutes of fine-tuning data, respectively. Furthermore, simulation benchmark studies show that obtaining stabilizing policies by optimizing our proposed costs requires orders of magnitude less data compared to standard cost designs.
