  Recently, Offline Reinforcement Learning (RL) has achieved remarkable
progress with the emergence of various algorithms and datasets. However, these
methods usually focus on algorithmic advancements, ignoring that many low-level
implementation choices considerably influence or even drive the final
performance. As a result, it becomes hard to attribute the progress in Offline
RL as these choices are not sufficiently discussed and aligned in the
literature. In addition, papers focusing on a dataset (e.g., D4RL) often ignore
algorithms proposed on another dataset (e.g., RL Unplugged), causing isolation
among the algorithms, which might slow down the overall progress. Therefore,
this work aims to bridge the gaps caused by low-level choices and datasets. To
this end, we empirically investigate 20 implementation choices using three
representative algorithms (i.e., CQL, CRR, and IQL) and present a guidebook for
choosing implementations. Following the guidebook, we find two variants CRR+
and CQL+ , achieving new state-of-the-art on D4RL. Moreover, we benchmark eight
popular offline RL algorithms across datasets under unified training and
evaluation framework. The findings are inspiring: the success of a learning
paradigm severely depends on the data distribution, and some previous
conclusions are biased by the dataset used. Our code is available at
https://github.com/sail-sg/offbench.
