Offline Dataset

Offline datasets in reinforcement learning (RL) are collections of pre-recorded agent-environment interactions used to train RL agents without requiring further online data collection. Current research focuses on mitigating challenges like data distribution shifts and inaccurate simulators by employing techniques such as diffusion models, generative adversarial networks (GANs), and pessimistic value iteration, often within multi-task learning frameworks. These advancements aim to improve the efficiency and robustness of offline RL, enabling the application of RL in scenarios where online data acquisition is expensive, dangerous, or impossible, with implications for robotics, healthcare, and other fields.