CORL is an Offline Reinforcement Learning library that provides high-quality and easy-to-follow single-file implementations of SOTA ORL algorithms. Each implementation is backed by a research-friendly codebase, allowing you to run or tune thousands of experiments. Heavily inspired by cleanrl for online RL, check them out too!
Single-file implementation
Benchmarked Implementation (11+ offline algorithms, 5+ offline-to-online algorithms, 30+ datasets with detailed logs)
Weights and Biases integration
Single-file implementation Benchmarked Implementation (11+ offline algorithms, 5+ offline-to-online algorithms, 30+ datasets with detailed logs) Weights and Biases integration