Reinforcement Learning methods with advanced optimization techniques
RLopt combines state-of-the-art reinforcement learning algorithms with advanced optimization techniques. This library provides efficient implementations for training RL agents with optimized performance.
- Integration with popular RL frameworks (Stable-Baselines3, TorchRL)
- Advanced optimization algorithms
- Configurable training pipelines using Hydra
- Experiment tracking with WandB
For training RL agents in IsaacLab, please refer to the IsaacLab documentation and our forked version.
- Python 3.10 or higher
- PyTorch
- CUDA-compatible GPU (recommended for faster training)
pip install .Stay tuned for the official documentation.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.