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README.md
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**Language**: [English](README.md) | [简体中文](README.zh-CN.md)
# MotrixLab
![GitHub License](https://img.shields.io/github/license/Motphys/MotrixLab)
![Python Version](https://img.shields.io/badge/python-3.10-blue)
`MotrixLab` 是一个基于 [MotrixSim](https://github.com/Motphys/motrixsim-docs) 仿真引擎的强化学习框架，专为机器人仿真和训练设计。该项目提供了一个完整的强化学习开发平台，集成了多种仿真环境和训练框架。
`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.
## 项目概述
## Project Overview
该项目分为两个核心部分：
The project is divided into two core components:
- **motrix_envs**: 基于 MotrixSim 构建的各种 RL 仿真环境，定义了 observation、action、reward。与具体的 RL 框架无关，目前支持 MotrixSim 的 CPU 后端
- **motrix_rl**: 集成 RL 框架，并使用 motrix_envs 里的各种环境参数进行训练。目前支持 SKRL 框架的 PPO 算法
- **motrix_envs**: Various RL simulation environments built on MotrixSim, defining observation, action, and reward. Framework-agnostic and currently supports MotrixSim's CPU backend
- **motrix_rl**: Integrates RL frameworks and uses various environment parameters from motrix_envs for training. Currently supports SKRL framework's PPO algorithm
> 文档地址：https://motrixlab.readthedocs.io
> Documentation: https://motrixlab.readthedocs.io
## 主要特性
## Key Features
- **统一接口**: 提供简洁统一的强化学习训练和评估接口
- **多后端支持**: 支持 JAX 和 PyTorch 训练后端，可根据硬件环境灵活选择
- **丰富环境**: 包含基础控制、运动、操作等多种机器人仿真环境
- **高性能仿真**: 基于 MotrixSim 的高性能物理仿真引擎
- **可视化训练**: 支持实时渲染和训练过程可视化
- **Unified Interface**: Provides a concise and unified reinforcement learning training and evaluation interface
- **Multi-backend Support**: Supports JAX and PyTorch training backends, with flexible selection based on hardware environment
- **Rich Environments**: Includes various robot simulation environments such as basic control, locomotion, and manipulation tasks
- **High-performance Simulation**: Built on MotrixSim's high-performance physics simulation engine
- **Visual Training**: Supports real-time rendering and training process visualization
## 🚀 快速开始
## 🚀 Quick Start
> 以下示例使用了 Python 项目管理工具：[UV](https://docs.astral.sh/uv/)
> The following examples use the Python project management tool: [UV](https://docs.astral.sh/uv/)
>
> 在开始之前，请先[安装](https://docs.astral.sh/uv/getting-started/installation/)该工具。
> Before starting, please [install](https://docs.astral.sh/uv/getting-started/installation/) this tool.
### 克隆仓库
### Clone Repository
```bash
git clone https://github.com/Motphys/MotrixLab
@@ -38,63 +40,63 @@ cd MotrixLab
git lfs pull
```
### 安装依赖
### Install Dependencies
安装全部依赖：
Install all dependencies:
```bash
uv sync --all-packages --all-extras
```
SKRL 框架支持 JAX(Flax)或 PyTorch 作为训练后端，您也可以根据自己的设备环境，选择只安装其中一种训练后端：
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:
安装 JAX 作为训练后端（仅支持 Linux 平台）：
Install JAX as training backend (Linux only):
```bash
uv sync --all-packages --extra skrl-jax
```
安装 PyTorch 作为训练后端：
Install PyTorch as training backend:
```bash
uv sync --all-packages --extra skrl-torch
```
## 🎯 使用指南
## 🎯 Usage Guide
### 环境可视化
### Environment Visualization
查看环境而不执行训练：
View environments without executing training:
```bash
uv run scripts/view.py --env cartpole
```
### 训练模型
### Model Training
```bash
uv run scripts/train.py --env cartpole
```
训练结果会保存在 `runs/{env-name}/` 目录下。
Training results are saved in the `runs/{env-name}/` directory.
通过 TensorBoard 查看训练数据：
View training data through TensorBoard:
```bash
uv run tensorboard --logdir runs/{env-name}
```
### 模型推理
### Model Inference
```
```bash
uv run scripts/play.py --env cartpole
```
更多使用方式请参考[用户文档](https://motrixlab.readthedocs.io)
For more usage methods, please refer to the [User Documentation](https://motrixlab.readthedocs.io)
## 📬 联系方式
## 📬 Contact
有问题或建议？欢迎通过以下方式联系我们：
Have questions or suggestions? Feel free to contact us through:
- GitHub Issues: [提交问题](https://github.com/Motphys/MotrixLab/issues)
- Discussions: [加入讨论](https://github.com/Motphys/MotrixLab/discussions)
- GitHub Issues: [Submit Issues](https://github.com/Motphys/MotrixLab/issues)
- Discussions: [Join Discussion](https://github.com/Motphys/MotrixLab/discussions)

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**语言**: [English](README.md) | [简体中文](README.zh-CN.md)
# MotrixLab
![GitHub License](https://img.shields.io/github/license/Motphys/MotrixLab)
![Python Version](https://img.shields.io/badge/python-3.10-blue)
`MotrixLab` 是一个基于 [MotrixSim](https://github.com/Motphys/motrixsim-docs) 仿真引擎的强化学习框架，专为机器人仿真和训练设计。该项目提供了一个完整的强化学习开发平台，集成了多种仿真环境和训练框架。
## 项目概述
该项目分为两个核心部分：
- **motrix_envs**: 基于 MotrixSim 构建的各种 RL 仿真环境，定义了 observation、action、reward。与具体的 RL 框架无关，目前支持 MotrixSim 的 CPU 后端
- **motrix_rl**: 集成 RL 框架，并使用 motrix_envs 里的各种环境参数进行训练。目前支持 SKRL 框架的 PPO 算法
> 文档地址：https://motrixlab.readthedocs.io
## 主要特性
- **统一接口**: 提供简洁统一的强化学习训练和评估接口
- **多后端支持**: 支持 JAX 和 PyTorch 训练后端，可根据硬件环境灵活选择
- **丰富环境**: 包含基础控制、运动、操作等多种机器人仿真环境
- **高性能仿真**: 基于 MotrixSim 的高性能物理仿真引擎
- **可视化训练**: 支持实时渲染和训练过程可视化
## 🚀 快速开始
> 以下示例使用了 Python 项目管理工具：[UV](https://docs.astral.sh/uv/)
>
> 在开始之前，请先[安装](https://docs.astral.sh/uv/getting-started/installation/)该工具。
### 克隆仓库
```bash
git clone https://github.com/Motphys/MotrixLab
cd MotrixLab
git lfs pull
```
### 安装依赖
安装全部依赖：
```bash
uv sync --all-packages --all-extras
```
SKRL 框架支持 JAX(Flax)或 PyTorch 作为训练后端，您也可以根据自己的设备环境，选择只安装其中一种训练后端：
安装 JAX 作为训练后端（仅支持 Linux 平台）：
```bash
uv sync --all-packages --extra skrl-jax
```
安装 PyTorch 作为训练后端：
```bash
uv sync --all-packages --extra skrl-torch
```
## 🎯 使用指南
### 环境可视化
查看环境而不执行训练：
```bash
uv run scripts/view.py --env cartpole
```
### 训练模型
```bash
uv run scripts/train.py --env cartpole
```
训练结果会保存在 `runs/{env-name}/` 目录下。
通过 TensorBoard 查看训练数据：
```bash
uv run tensorboard --logdir runs/{env-name}
```
### 模型推理
```
uv run scripts/play.py --env cartpole
```
更多使用方式请参考[用户文档](https://motrixlab.readthedocs.io)
## 📬 联系方式
有问题或建议？欢迎通过以下方式联系我们：
- GitHub Issues: [提交问题](https://github.com/Motphys/MotrixLab/issues)
- Discussions: [加入讨论](https://github.com/Motphys/MotrixLab/discussions)

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docs/source/zh_CN/user_guide/getting_started/installation.md
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@@ -1,9 +1,13 @@
# 安装环境
## 安装要求
本文档将引导您完成 MotrixLab 的安装与配置。请仔细阅读系统要求，并根据您的使用场景选择合适的安装方式。
## 系统要求
- **Python 版本**：{bdg-danger-line}`3.10.*`
本项目依赖特定 Python 版本，其他版本暂不受支持：
| Python 版本 | 支持状态 |
| :---------: | :------: |
| ≤ 3.9 | ❌ |
@@ -11,7 +15,8 @@
| ≥ 3.11 | ❌ |
- **包管理器**：{bdg-danger-line}`UV`
[UV 安装参考](https://docs.astral.sh/uv/getting-started/installation/)
本项目采用 UV 作为唯一的包管理工具，以提供快速、可复现的依赖管理环境。UV 的安装方法请参考[官方文档](https://docs.astral.sh/uv/getting-started/installation/)。
- **系统及架构**：
@@ -19,7 +24,7 @@
- {bdg-danger-line}`Linux(x86_64)`
```{note}
各平台支持的功能如下：
不同操作系统对 MotrixLab 各功能模块的支持情况如下：
| 操作系统 | CPU 仿真 | 交互式查看器 | GPU 仿真 |
| :------: | :------: | :----------: | :------: |
@@ -27,25 +32,55 @@
| Windows | ✅ | ✅ | 🛠️ 开发中 |
```
## 安装方法
## 安装步骤
### 克隆项目
### 克隆项目仓库
```bash
git clone https://github.com/Motphys/MotrixLab.git
cd MotrixLab
```
### 安装依赖
### 配置依赖环境
使用 UV 安装项目依赖：
:::{dropdown} 配置国内镜像源（可选）
:animate: fade-in
:color: warning
:icon: desktop-download
如果您身处中国大陆，建议配置国内镜像源以加速依赖下载：
1. 修改项目根目录的 `uv.toml` 文件
```toml
[[index]]
name = "mirror"
# 请填写您选择的国内镜像源，例如：
# 清华源: "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
url = ""
[[index]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu128"
default = true
```
2. 在执行 `uv sync` 命令时添加 `--index-strategy unsafe-best-match` 参数：
```
uv sync --all-packages --all-extras --index-strategy unsafe-best-match
```
:::
执行以下命令安装完整依赖：
```bash
# 安装所有依赖
uv sync --all-packages --all-extras
```
如果只需要安装一种训练后端，可以选择单独安装指定的后端类型：
如果仅需特定训练框架，可选择性安装以减少依赖体积：
```bash

2
motrix_envs/pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "uv_build"
[project]
name = "motrix-envs"
version = "0.0.1"
version = "0.0.2"
description = "Robot simulation environment library based on MotrixSim providing multi-task RL environments."
authors = [{ name = "Motphys", email = "developers@motphys.com" }]
requires-python = "==3.10.*"

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motrix_rl/pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "uv_build"
[project]
name = "motrix-rl"
version = "0.0.1"
version = "0.0.2"
description = "Reinforcement learning training framework based on SKRL with multi-backend unified training interface."
authors = [{ name = "Motphys", email = "developers@motphys.com" }]
requires-python = "==3.10.*"
@@ -17,6 +17,7 @@ skrl-jax = [
"skrl===1.4.3; sys_platform == 'linux'",
"jax[cuda12]==0.4.34; sys_platform == 'linux'",
"flax===0.10.4; sys_platform == 'linux'",
"tensorflow===2.20.0; sys_platform == 'linux'",
]
skrl-torch = [
"skrl===1.4.3",

2
pyproject.toml
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@@ -1,6 +1,6 @@
[project]
name = "motrix-lab"
version = "0.0.1"
version = "0.0.2"
description = "A general-purpose machine learning architecture designed for robot training"
authors = [{ name = "Motphys", email = "developers@motphys.com" }]
requires-python = "==3.10.*"

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