The field of reinforcement learning is moving towards addressing complex tasks in continuous-time settings, with a focus on efficient learning and optimal decision-making strategies. Recent developments have explored the application of transfer learning and policy transfer to enhance the efficiency of reinforcement learning algorithms. Theoretical guarantees and algorithmic benefits of transfer learning in continuous-time RL have been established, addressing a gap in existing literature. Additionally, novel policy learning algorithms have been introduced, achieving global linear and local super-linear convergence. The characterization of continuous-time Q-functions via martingale conditions and the linking of diffusion policy scores to action gradients have also been investigated. Noteworthy papers include:

• Policy Transfer Ensures Fast Learning for Continuous-Time LQR with Entropy Regularization, which provides the first theoretical proof of policy transfer for continuous-time RL.
• Continuous Q-Score Matching: Diffusion Guided Reinforcement Learning for Continuous-Time Control, which introduces a novel RL method for continuous-time control.