chore: release v0.0.2

README.md

@@ -1,34 +1,36 @@
+**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_envs**: Various RL simulation environments built on MotrixSim, defining observation, action, and reward. Framework-agnostic and currently supports MotrixSim's CPU backend
--   **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_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
+-   **Unified Interface**: Provides a concise and unified reinforcement learning training and evaluation interface
--   **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
+-   **Multi-backend Support**: Supports JAX and PyTorch training backends, with flexible selection based on hardware environment
--   **丰富环境**: 包含基础控制、运动、操作等多种机器人仿真环境
+-   **Rich Environments**: Includes various robot simulation environments such as basic control, locomotion, and manipulation tasks
--   **高性能仿真**: 基于 MotrixSim 的高性能物理仿真引擎
+-   **High-performance Simulation**: Built on MotrixSim's high-performance physics simulation engine
--   **可视化训练**: 支持实时渲染和训练过程可视化
+-   **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)
+-   GitHub Issues: [Submit Issues](https://github.com/Motphys/MotrixLab/issues)
--   Discussions: [加入讨论](https://github.com/Motphys/MotrixLab/discussions)
+-   Discussions: [Join Discussion](https://github.com/Motphys/MotrixLab/discussions)

README.zh-CN.md

@@ -0,0 +1,102 @@
+**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)

docs/source/zh_CN/user_guide/getting_started/installation.md

@@ -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
 

motrix_envs/pyproject.toml

@@ -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.*"

motrix_rl/pyproject.toml

@@ -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",

pyproject.toml

@@ -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.*"

uv.lock

@@ -43,6 +43,19 @@ wheels = [
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 ]
 
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+    { name = "wheel" },
+]
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+wheels = [
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+]
+
 [[package]]
 name = "autodocsumm"
 version = "0.2.14"
@@ -185,6 +198,15 @@ wheels = [
     { url = "https://download.pytorch.org/whl/filelock-3.19.1-py3-none-any.whl" },
 ]
 
+[[package]]
+name = "flatbuffers"
+version = "25.9.23"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/1f/3ee70b0a55137442038f2a33469cc5fddd7e0ad2abf83d7497c18a2b6923/flatbuffers-25.9.23.tar.gz", hash = "sha256:676f9fa62750bb50cf531b42a0a2a118ad8f7f797a511eda12881c016f093b12", size = 22067 }
+wheels = [
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+]
+
 [[package]]
 name = "flax"
 version = "0.10.4"
@@ -213,6 +235,27 @@ wheels = [
     { url = "https://download.pytorch.org/whl/fsspec-2025.9.0-py3-none-any.whl" },
 ]
 
+[[package]]
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+version = "0.6.0"
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+]
+
+[[package]]
+name = "google-pasta"
+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
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+wheels = [
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+]
+
 [[package]]
 name = "grpcio"
 version = "1.76.0"
@@ -249,6 +292,21 @@ wheels = [
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 ]
 
+[[package]]
+name = "h5py"
+version = "3.15.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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+]
+
 [[package]]
 name = "humanize"
 version = "4.14.0"
@@ -375,6 +433,37 @@ wheels = [
     { url = "https://download.pytorch.org/whl/jinja2-3.1.6-py3-none-any.whl" },
 ]
 
+[[package]]
+name = "keras"
+version = "3.12.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "absl-py" },
+    { name = "h5py" },
+    { name = "ml-dtypes" },
+    { name = "namex" },
+    { name = "numpy" },
+    { name = "optree" },
+    { name = "packaging" },
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