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

Overview

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

Venue. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems