Policy Transfer Ensures Fast Learning for Continuous-Time LQR with Entropy Regularization

• URL: http://arxiv.org/abs/2510.15165v1
• Date: Thu, 16 Oct 2025 21:57:53 GMT
• Title: Policy Transfer Ensures Fast Learning for Continuous-Time LQR with Entropy Regularization
• Authors: Xin Guo, Zijiu Lyu,
• Abstract summary: 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.<n>This paper investigates policy transfer, a TL approach that initializes learning in a target RL task using a policy from a related source task.<n>We introduce a novel policy learning algorithm for continuous-time LQRs that achieves global linear and local super-linear convergence.
• Score: 2.494814157306265
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 novel 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 continuous-time RL, addressing a gap in existing literature and extending prior work from discrete to continuous time settings. As a byproduct of our analysis, we derive the stability of a class of continuous-time score-based diffusion models via their connection with LQRs.

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