The Impact of Approximation Errors on Warm-Start Reinforcement Learning: A Finite-time AnalysisDownload PDF


22 Sept 2022, 12:32 (modified: 14 Nov 2022, 23:28)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: Reinforcement Learning, Finite-time Analysis, Approximation Error, Warm Start
Abstract: Warm-Start reinforcement learning (RL), aided by a prior policy obtained from offline training, is emerging as a promising RL approach for practical applications. Recent empirical studies have demonstrated that the performance of Warm-Start RL can be improved \textit{quickly} in some cases but become \textit{stagnant} in other cases, calling for a fundamental understanding, especially when the function approximation is used. To fill this void, we take a finite time analysis approach to quantify the impact of approximation errors on the learning performance of Warm-Start RL. Specifically, we consider the widely used Actor-Critic (A-C) method with a prior policy. We first quantify the approximation errors in the Actor update and the Critic update, respectively. Next, we cast the Warm-Start A-C algorithm as Newton's method with perturbation, and study the impact of the approximation errors on the finite-time learning performance with inaccurate Actor/Critic updates. Under some general technical conditions, we obtain lower bounds on the sub-optimality gap of the Warm-Start A-C algorithm to quantify the impact of the bias and error propagation. We also derive the upper bounds, which provide insights on achieving the desired finite-learning performance in the Warm-Start A-C algorithm.
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