PyPI 官网下载 | mujoco-py-1.50.1.39.tar.gz

# mujoco-py [](https://openai.github.io/mujoco-py/build/html/index.html) [](https://travis-ci.org/openai/mujoco-py) [](https://ci.appveyor.com/project/wojzaremba/mujoco-py) [MuJoCo](http://mujoco.org/) is a physics engine for detailed, efficient rigid body simulations with contacts. `mujoco-py` allows using MuJoCo from Python 3. ## Synopsis ### Requirements The following platforms are currently supported: - Linux with Python 3.5.2. See [the `Dockerfile`](Dockerfile) for the canonical list of system dependencies. Support for Python 3.6 [is planned](https://github.com/openai/mujoco-py/issues/52). - OS X with Python 3.5.2. Support for Python 3.6 [is planned](https://github.com/openai/mujoco-py/issues/52). - Windows (experimental) with Python 3.5.2. See [the Appveyor file](https://github.com/openai/mujoco-py/blob/master/.appveyor.yml#L16-L32) for the canonical list of dependencies. Python 2 has been desupported since [1.50.1.0](https://github.com/openai/mujoco-py/releases/tag/1.50.1.0). Python 2 users can stay on the [`0.5` branch](https://github.com/openai/mujoco-py/tree/0.5). The latest release there is [`0.5.7`](https://github.com/openai/mujoco-py/releases/tag/0.5.7) which can be installed with `pip install mujoco-py==0.5.7`. ### Install MuJoCo 1. Obtain a 30-day free trial on the [MuJoCo website](https://www.roboti.us/license.html) or free license if you are a student. The license key will arrive in an email with your username and password. 2. Download the MuJoCo version 1.50 binaries for [Linux](https://www.roboti.us/download/mjpro150_linux.zip), [OSX](https://www.roboti.us/download/mjpro150_osx.zip), or [Windows](https://www.roboti.us/download/mjpro150_windows.zip). 3. Unzip the downloaded `mjpro150` directory into `~/.mujoco/mjpro150`, and place your license key (the `mjkey.txt` file from your email) at `~/.mujoco/mjkey.txt`. ### Install and use `mujoco-py` To include `mujoco-py` in your own package, add it to your requirements like so: ``` mujoco-py<1.50.2,>=1.50.1 ``` To play with `mujoco-py` interactively, follow these steps: ``` $ pip3 install -U 'mujoco-py<1.50.2,>=1.50.1' $ python3 import mujoco_py from os.path import dirname model = mujoco_py.load_model_from_path(dirname(dirname(mujoco_py.__file__)) +"/xmls/claw.xml") sim = mujoco_py.MjSim(model) print(sim.data.qpos) # [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] sim.step() print(sim.data.qpos) # [ 2.09217903e-06 -1.82329050e-12 -1.16711384e-07 -4.69613872e-11 # -1.43931860e-05 4.73350204e-10 -3.23749942e-05 -1.19854057e-13 # -2.39251380e-08 -4.46750545e-07 1.78771599e-09 -1.04232280e-08] ``` See the [full documentation](https://openai.github.io/mujoco-py/build/html/index.html) for advanced usage. ### Missing GLFW A common error when installing is: raise ImportError("Failed to load GLFW3 shared library.") Which happens when the `glfw` python package fails to find a GLFW dynamic library. MuJoCo ships with its own copy of this library, which can be used during installation. Add the path to the mujoco bin directory to your dynamic loader: LD_LIBRARY_PATH=$HOME/.mujoco/mjpro150/bin pip install mujoco-py This is particularly useful on Ubuntu 14.04, which does not have a GLFW package. ## Usage Examples A number of examples demonstrating some advanced features of `mujoco-py` can be found in [`examples/`](/./examples/). These include: - [`body_interaction.py`](./examples/body_interaction.py): shows interactions between colliding bodies - [`disco_fetch.py`](./examples/disco_fetch.py): shows how `TextureModder` can be used to randomize object textures - [`internal_functions.py`](./examples/internal_functions.py): shows how to call raw mujoco functions like `mjv_room2model` - [`markers_demo.py`](./examples/markers_demo.py): shows how to add visualization-only geoms to the viewer - [`serialize_model.py`](./examples/serialize_model.py): shows how to save and restore a model - [`setting_state.py`](./examples/setting_state.py): shows how to reset the simulation to a given state - [`simpool.py`](./examples/simpool.py): shows how `MjSimPool` can be used to run a number of simulations in parallel - [`tosser.py`](./examples/tosser.py): shows a simple actuated object sorting robot application See the [full documentation](https://openai.github.io/mujoco-py/build/html/index.html) for advanced usage. ## Development To run the provided unit and integrations tests: ``` make test ``` To test GPU-backed rendering, run: ``` make test_gpu ``` This is somewhat dependent on internal OpenAI infrastructure at the moment, but it should run if you change the `Makefile` parameters for your own setup. ## Credits `mujoco-py` is maintained by the OpenAI Robotics team. Contributors include: - Alex Ray - Bob McGrew - Jonas Schneider - Jonathan Ho - Peter Welinder - Wojciech Zaremba