  CORL is an open-source library that provides thoroughly benchmarked
single-file implementations of both deep offline and offline-to-online
reinforcement learning algorithms. It emphasizes a simple developing experience
with a straightforward codebase and a modern analysis tracking tool. In CORL,
we isolate methods implementation into separate single files, making
performance-relevant details easier to recognize. Additionally, an experiment
tracking feature is available to help log metrics, hyperparameters,
dependencies, and more to the cloud. Finally, we have ensured the reliability
of the implementations by benchmarking commonly employed D4RL datasets
providing a transparent source of results that can be reused for robust
evaluation tools such as performance profiles, probability of improvement, or
expected online performance.
