A Rank-Based Sampling Framework For Offline Reinforcement Learning

Offline reinforcement learning (RL) is an attractive method that learns a policy purely from a previously collected dataset without additional interaction. However, it suffers the data quality issue that the performance of the policy is largely dependent on the previously collected dataset. In this paper, we propose a novel and robust sampling technique, Rank-Based sampling (RBS) to address this i...