A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems

Understand

With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents.

• However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment.
• Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets.
• Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics.