Policy Transfer for Continuous-Time Reinforcement Learning: A (Rough) Differential Equation Approach

The recent paper titled 'Policy Transfer for Continuous-Time Reinforcement Learning: A (Rough) Differential Equation Approach' presents a groundbreaking study on policy transfer, a technique widely used in transfer learning for large language models. It focuses on two classes of continuous-time reinforcement learning problems: linear-quadratic systems with Shannon's entropy regularization and systems with non-linear, bounded dynamics. The authors establish the stability of diffusion stochastic differential equations using rough path theory, marking the first theoretical proof of policy transfer in continuous-time reinforcement learning. This proof indicates that an optimal policy learned for one problem can serve as a strong starting point for finding a near-optimal policy in a related problem, while preserving the convergence rates of the original algorithm. To illustrate the practical benefits of this approach, the authors propose a novel policy learning algorithm for continuous-time…