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CORL (Collection of Reinforcement Learning Environments for Control Tasks) is a modular and extensible set of high-quality reinforcement learning environments focused on continuous control and robotics. It aims to offer standardized environments suitable for benchmarking state-of-the-art RL algorithms in control tasks, including physics-based simulations and custom-designed scenarios.
Features
- Collection of continuous control and robotics-focused environments
- Designed for benchmarking and testing RL algorithms
- Supports Gym and Gymnasium API standards for easy integration
- Provides physics-based environments using MuJoCo and Bullet
- Includes simple and complex tasks from balancing to locomotion
- Extensible for creating custom control environments
Project Samples
Categories
Reinforcement Learning LibrariesLicense
Apache License V2.0Follow CORL
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