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.

翻译：CORL是一个开源库，该库提供了经过全面基准测试的深度离线及离线到在线强化学习算法的单文件实现。它通过简洁的代码库和现代分析追踪工具，强调简洁的开发体验。在CORL中，我们将方法的实现隔离到独立的单文件中，使与性能相关的细节更易于识别。此外，还提供了实验追踪功能，用于记录指标、超参数、依赖关系等到云端。最后，我们通过对广泛使用的D4RL数据集进行基准测试，确保了实现的可靠性，提供了可复用的透明结果来源，用于构建稳健的评估工具，如性能轮廓、改进概率或期望在线性能。