Offline Data

Offline data in reinforcement learning (RL) focuses on training RL agents using pre-collected datasets, eliminating the need for costly online interaction with the environment. Current research emphasizes overcoming challenges like data bias and distribution shifts, employing techniques such as hierarchical RL, diffusion models, and metric learning to improve policy learning from diverse and potentially suboptimal offline data. This field is crucial for deploying RL in high-stakes applications like robotics and healthcare, where online exploration is impractical or unsafe, and advancements are driving progress in sample efficiency and robustness of RL algorithms.