Offline RL

A trajectory perspective on the role of data sampling techniques in offline reinforcement learning

Organizing samples in a trajective manner can improve the learning efficiency for offline RL algorithms.

May 1, 2024

ENOTO: improving offline-to-online reinforcement learning with Q-ensembles

Jan 1, 2024