Policy transfer ensures fast learning for continuous-time LQR with entropy regularization

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• Xin Guo (University of California, Berkeley)
• Tuesday 11 November 2025, 09:00-09:40
• Seminar Room 1, Newton Institute.

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SCLW01 - Bridging Stochastic Control And Reinforcement Learning: Theories and Applications

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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