Offline Meta-RL

Offline Meta-RL is an emerging research direction that combines offline reinforcement learning (Offline RL) and meta-reinforcement learning (Meta-RL). This concept was first proposed in 2020 by the DeepMind research team and published in the paper “Offline Meta Reinforcement LearningIt aims to use offline data (i.e. pre-collected data that does not rely on online interactions) to train models so that they can quickly adapt to new tasks or new environments without a large amount of online interactions. This approach is particularly suitable for scenarios where online interactions are costly or risky, such as medical care, autonomous driving, and other fields.