Abstract

Reinforcement Learning (RL) enables agents to learn optimal decision-making strategies through interaction with an environment, yet training from scratch on complex tasks can be highly inefficient. Transfer learning (TL), widely successful in large language models (LLMs), offers a promising direction for enhancing RL efficiency by leveraging pre-trained models. This paper investigates policy transfer—a TL approach that initializes learning in a target RL task using a policy from a related source task—in the context of continuous-time linear quadratic regulators (LQRs) with entropy regularization. We provide the first theoretical proof of policy transfer for continuous-time RL, proving that a policy optimal for one LQR serves as a near-optimal initialization for closely related LQRs, while preserving the original algorithm’s convergence rate. Furthermore, we introduce a policy learning algorithm for continuous-time LQRs that achieves global linear and local super-linear convergence. Our results demonstrate both theoretical guarantees and algorithmic benefits of transfer learning in continuoustime RL, addressing a gap in existing literature and extending prior work from discrete to continuous settings.