The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
Abstract¶
A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e.g. iLQR - on the learned model to minimize a target cost. This paper conducts a rigorous analysis of a simplified variant of this strategy for general nonlinear systems. We analyze an algorithm which iterates between estimating local linear models of nonlinear system dynamics and performing iLQR-like policy updates. We demonstrate that this algorithm attains sample complexity polynomial in relevant problem parameters, and, by synthesizing locally stabilizing gains, overcomes exponential dependence in problem horizon. Experimental results validate the performance of our algorithm, and compare to natural deep-learning baselines.
Summary¶
Provides theoretical grounding for why locally linear models learned from data combined with model-based trajectory optimization (iLQR) can achieve sample efficiency for control of nonlinear systems with unknown dynamics.