Lianghui Liu

Meta-Normalizing Flow for Data-Limited Offline Meta-Reinforcement Learning (Student Abstract)

Offline Meta-Reinforcement Learning (OMRL) leverages pre-collected data to adapt to new tasks. Context-based methods learn task representations from contexts. However, the context is influenced by both the task and the behavior policy. The mismatch between the behavior policy and the testing policy