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Apr. 19, 2019
May. 24, 2017

Status: Active (under active development, breaking changes may occur)

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OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms.

These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones.


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


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

Virtual environment

From the general python package sanity perspective, it is a good idea to use virtual environments (virtualenvs) to make sure packages from different projects do not interfere with each other. You can install virtualenv (which is itself a pip package) via

pip install virtualenv

Virtualenvs are essentially folders that have copies of python executable and all python packages. To create a virtualenv called venv with python3, one runs

virtualenv /path/to/venv --python=python3

To activate a virtualenv:

. /path/to/venv/bin/activate

More thorough tutorial on virtualenvs and options can be found here


  • Clone the repo and cd into it:

    git clone
    cd baselines
  • If you don't have TensorFlow installed already, install your favourite flavor of TensorFlow. In most cases,

    pip install tensorflow-gpu # if you have a CUDA-compatible gpu and proper drivers


    pip install tensorflow

    should be sufficient. Refer to TensorFlow installation guide for more details.

  • Install baselines package

    pip install -e .


Some of the baselines examples use MuJoCo (multi-joint dynamics in contact) physics simulator, which is proprietary and requires binaries and a license (temporary 30-day license can be obtained from Instructions on setting up MuJoCo can be found here

Testing the installation

All unit tests in baselines can be run using pytest runner:

pip install pytest

Training models

Most of the algorithms in baselines repo are used as follows:

python -m --alg=<name of the algorithm> --env=<environment_id> [additional arguments]

Example 1. PPO with MuJoCo Humanoid

For instance, to train a fully-connected network controlling MuJoCo humanoid using PPO2 for 20M timesteps

python -m --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7

Note that for mujoco environments fully-connected network is default, so we can omit --network=mlp The hyperparameters for both network and the learning algorithm can be controlled via the command line, for instance:

python -m --alg=ppo2 --env=Humanoid-v2 --network=mlp --num_timesteps=2e7 --ent_coef=0.1 --num_hidden=32 --num_layers=3 --value_network=copy

will set entropy coefficient to 0.1, and construct fully connected network with 3 layers with 32 hidden units in each, and create a separate network for value function estimation (so that its parameters are not shared with the policy network, but the structure is the same)

See docstrings in common/ for description of network parameters for each type of model, and docstring for baselines/ppo2/ for the description of the ppo2 hyperparameters.

Example 2. DQN on Atari

DQN with Atari is at this point a classics of benchmarks. To run the baselines implementation of DQN on Atari Pong:

python -m --alg=deepq --env=PongNoFrameskip-v4 --num_timesteps=1e6

Saving, loading and visualizing models

The algorithms serialization API is not properly unified yet; however, there is a simple method to save / restore trained models. --save_path and --load_path command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively. Let's imagine you'd like to train ppo2 on Atari Pong, save the model and then later visualize what has it learnt.

python -m --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=2e7 --save_path=~/models/pong_20M_ppo2

This should get to the mean reward per episode about 20. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize:

python -m --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --load_path=~/models/pong_20M_ppo2 --play

NOTE: At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in baselines/common/vec_env/ This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default

Loading and vizualizing learning curves and other training metrics

See here for instructions on how to load and display the training data.



Results of benchmarks on Mujoco (1M timesteps) and Atari (10M timesteps) are available here for Mujoco and here for Atari respectively. Note that these results may be not on the latest version of the code, particular commit hash with which results were obtained is specified on the benchmarks page.

To cite this repository in publications:

  author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai and Zhokhov, Peter},
  title = {OpenAI Baselines},
  year = {2017},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{}},