CORL: Research-oriented Deep Offline Reinforcement Learning Library
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Offline Reinforcement Learning | Maze2D medium v1 | Normalized Return0.79 | 30 | |
| Offline Reinforcement Learning | Maze2D large v1 | Normalized Return2.26 | 30 | |
| Planning and Control | maze2d-umaze v1 (100 episodes, 300 steps/ep) | Score0.36 | 16 | |
| Offline Reinforcement Learning | AntMaze Medium-Diverse v2 | Average Score0.008 | 14 | |
| Offline Reinforcement Learning | AntMaze medium-play v2 | Average Score0.00e+0 | 14 | |
| Offline Reinforcement Learning | AntMaze large-play v2 | D4RL Score0.00e+0 | 11 | |
| Offline Reinforcement Learning | AntMaze large-diverse v2 | D4RL Score0.00e+0 | 11 |