Baselines requires python3 (>=3.5) with the development headers. You’ll also need system packages CMake, OpenMPI and zlib. Those can be installed as follows


Stable-Baselines supports Tensorflow versions from 1.8.0 to 1.15.0, and does not work on Tensorflow versions 2.0.0 and above. PyTorch support is done in Stable-Baselines3


sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev

Mac OS X

Installation of system packages on Mac requires Homebrew. With Homebrew installed, run the following:

brew install cmake openmpi

Windows 10

We recommend using Anaconda for Windows users for easier installation of Python packages and required libraries. You need an environment with Python version 3.5 or above.

For a quick start you can move straight to installing Stable-Baselines in the next step (without MPI). This supports most but not all algorithms.

To support all algorithms, Install MPI for Windows (you need to download and install msmpisetup.exe) and follow the instructions on how to install Stable-Baselines with MPI support in following section.


Trying to create Atari environments may result to vague errors related to missing DLL files and modules. This is an issue with atari-py package. See this discussion for more information.

Stable Release

To install with support for all algorithms, including those depending on OpenMPI, execute:

pip install stable-baselines[mpi]

GAIL, DDPG, TRPO, and PPO1 parallelize training using OpenMPI. OpenMPI has had weird interactions with Tensorflow in the past (see Issue #430) and so if you do not intend to use these algorithms we recommend installing without OpenMPI. To do this, execute:

pip install stable-baselines

If you have already installed with MPI support, you can disable MPI by uninstalling mpi4py with pip uninstall mpi4py.


Unless you are using the bleeding-edge version, you need to install the correct Tensorflow version manually. See Issue #849

Bleeding-edge version

To install the latest master version:

pip install git+

Development version

To contribute to Stable-Baselines, with support for running tests and building the documentation.

git clone && cd stable-baselines
pip install -e .[docs,tests,mpi]

Using Docker Images

If you are looking for docker images with stable-baselines already installed in it, we recommend using images from RL Baselines Zoo.

Otherwise, the following images contained all the dependencies for stable-baselines but not the stable-baselines package itself. They are made for development.

Use Built Images

GPU image (requires nvidia-docker):

docker pull stablebaselines/stable-baselines

CPU only:

docker pull stablebaselines/stable-baselines-cpu

Build the Docker Images

Build GPU image (with nvidia-docker):

make docker-gpu

Build CPU image:

make docker-cpu

Note: if you are using a proxy, you need to pass extra params during build and do some tweaks:

--network=host --build-arg HTTP_PROXY= --build-arg http_proxy= --build-arg HTTPS_PROXY= --build-arg https_proxy=

Run the images (CPU/GPU)

Run the nvidia-docker GPU image

docker run -it --runtime=nvidia --rm --network host --ipc=host --name test --mount src="$(pwd)",target=/root/code/stable-baselines,type=bind stablebaselines/stable-baselines bash -c 'cd /root/code/stable-baselines/ && pytest tests/'

Or, with the shell file:

./scripts/ pytest tests/

Run the docker CPU image

docker run -it --rm --network host --ipc=host --name test --mount src="$(pwd)",target=/root/code/stable-baselines,type=bind stablebaselines/stable-baselines-cpu bash -c 'cd /root/code/stable-baselines/ && pytest tests/'

Or, with the shell file:

./scripts/ pytest tests/

Explanation of the docker command:

  • docker run -it create an instance of an image (=container), and run it interactively (so ctrl+c will work)
  • --rm option means to remove the container once it exits/stops (otherwise, you will have to use docker rm)
  • --network host don’t use network isolation, this allow to use tensorboard/visdom on host machine
  • --ipc=host Use the host system’s IPC namespace. IPC (POSIX/SysV IPC) namespace provides separation of named shared memory segments, semaphores and message queues.
  • --name test give explicitly the name test to the container, otherwise it will be assigned a random name
  • --mount src=... give access of the local directory (pwd command) to the container (it will be map to /root/code/stable-baselines), so all the logs created in the container in this folder will be kept
  • bash -c '...' Run command inside the docker image, here run the tests (pytest tests/)