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CORL is an open-source library that provides thoroughly benchmarkedsingle-file implementations of both deep offline and offline-to-onlinereinforcement learning algorithms. It emphasizes a simple developing experiencewith a straightforward codebase and a modern analysis tracking tool. In CORL,we isolate methods implementation into separate single files, makingperformance-relevant details easier to recognize. Additionally, an experimenttracking feature is available to help log metrics, hyperparameters,dependencies, and more to the cloud. Finally, we have ensured the reliabilityof the implementations by benchmarking commonly employed D4RL datasetsproviding a transparent source of results that can be reused for robustevaluation tools such as performance profiles, probability of improvement, orexpected online performance.

CORL: Research-oriented Deep Offline Reinforcement Learning Library