Offline reinforcement learning methods for real-world problems

Offline learning, also known as batch learning or offline training, describes training machine learning models using a fixed dataset that is not updated with new data in real time. Offline Reinforcement Learning is a popular approach for training agents in real-world scenarios, such as robotics, autonomous driving, and healthcare, where direct environmental interaction or building a highly accurate simulator is not feasible. However, Offline RL experiences several challenges due to the absence of real-time feedback from the online environment. The major challenges are distribution shift, bootstrapping error, and out-of-distribution error. Various methods are used in Offline RL and can be categorized according to the type of function approximator they utilize. These methods include value-regularized, policy-constraint, model-based, and uncertainty-based methods. Additionally, several techniques exist to apply offline RL in the real world and overcome the limitations of a fixed dataset by looking beyond the offline dataset. These techniques include optimism learning and domain generalization. The field of Offline RL is rapidly evolving, and there is still much to be done to overcome its limitations and expand its capabilities. However, the potential benefits of Offline RL, such as the ability to train systems using existing data, make it a promising area for future research and development.