Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control

arXiv:2403.14860v1 Announce Type: cross
Abstract: We introduce $\mathcal{L}_1$-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input. Our approach generates a series of approximate control-affine models of the learned transition function according to the proposed switching law. Using the approximate model, control input produced by the underlying MBRL is perturbed by the $\mathcal{L}_1$ adaptive control, which is …

abstract algorithms arxiv augmentation control cs.lg cs.sy data design eess.sy free function learn reinforcement reinforcement learning robust series transition type