Offline to Online Reinforcement Learning

Offline-to-online reinforcement learning (RL) aims to improve the efficiency and safety of RL by leveraging pre-trained policies from offline datasets for online fine-tuning. Current research focuses on addressing challenges like distribution shift between offline and online data, improving the accuracy of Q-value estimation, and developing more robust exploration strategies, often employing techniques like diffusion models, Bayesian methods, and ensemble approaches. This hybrid approach holds significant promise for real-world applications where data acquisition is expensive or risky, enabling more efficient and reliable learning in domains such as robotics and autonomous systems.