103 lines
3.1 KiB
Markdown
103 lines
3.1 KiB
Markdown
**Language**: [English](README.md) | [简体中文](README.zh-CN.md)
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# MotrixLab
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`MotrixLab` is a reinforcement learning framework based on the [MotrixSim](https://github.com/Motphys/motrixsim-docs) simulation engine, designed specifically for robot simulation and training. This project provides a complete reinforcement learning development platform that integrates multiple simulation environments and training frameworks.
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## Project Overview
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The project is divided into two core components:
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- **motrix_envs**: Various RL simulation environments built on MotrixSim, defining observation, action, and reward. Framework-agnostic and currently supports MotrixSim's CPU backend
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- **motrix_rl**: Integrates RL frameworks and uses various environment parameters from motrix_envs for training. Currently supports SKRL framework's PPO algorithm
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> Documentation: https://motrixlab.readthedocs.io
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## Key Features
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- **Unified Interface**: Provides a concise and unified reinforcement learning training and evaluation interface
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- **Multi-backend Support**: Supports JAX and PyTorch training backends, with flexible selection based on hardware environment
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- **Rich Environments**: Includes various robot simulation environments such as basic control, locomotion, and manipulation tasks
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- **High-performance Simulation**: Built on MotrixSim's high-performance physics simulation engine
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- **Visual Training**: Supports real-time rendering and training process visualization
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## 🚀 Quick Start
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> The following examples use the Python project management tool: [UV](https://docs.astral.sh/uv/)
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>
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> Before starting, please [install](https://docs.astral.sh/uv/getting-started/installation/) this tool.
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### Clone Repository
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```bash
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git clone https://github.com/Motphys/MotrixLab
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cd MotrixLab
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git lfs pull
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```
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### Install Dependencies
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Install all dependencies:
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```bash
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uv sync --all-packages --all-extras
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```
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SKRL framework supports JAX(Flax) or PyTorch as training backends. You can also choose to install only one training backend based on your hardware environment:
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Install JAX as training backend (Linux only):
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```bash
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uv sync --all-packages --extra skrl-jax
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```
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Install PyTorch as training backend:
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```bash
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uv sync --all-packages --extra skrl-torch
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```
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## 🎯 Usage Guide
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### Environment Visualization
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View environments without executing training:
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```bash
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uv run scripts/view.py --env cartpole
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```
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### Model Training
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```bash
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uv run scripts/train.py --env cartpole
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```
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Training results are saved in the `runs/{env-name}/` directory.
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View training data through TensorBoard:
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```bash
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uv run tensorboard --logdir runs/{env-name}
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```
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### Model Inference
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```bash
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uv run scripts/play.py --env cartpole
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```
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For more usage methods, please refer to the [User Documentation](https://motrixlab.readthedocs.io)
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## 📬 Contact
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Have questions or suggestions? Feel free to contact us through:
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- GitHub Issues: [Submit Issues](https://github.com/Motphys/MotrixLab/issues)
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- Discussions: [Join Discussion](https://github.com/Motphys/MotrixLab/discussions)
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