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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
| Date | Stars |
|---|---|
| 2026-07-31 | 16748 |
| 2026-08-01 | 16747 |
| 2026-08-02 | 16746 |
| 2026-08-03 | 16746 |
| 2026-08-04 | 16747 |
| 2026-08-05 | 16749 |
| 2026-08-06 | 16749 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
**Status:** Maintenance (expect bug fixes and minor updates)
<img src="data/logo.jpg" width=25% align="right" /> [](https://travis-ci.org/openai/baselines)
# Baselines
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.
## Prerequisites
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
### Ubuntu
```bash
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](https://brew.sh). With Homebrew installed, run the following:
```bash
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
```bash
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
```bash
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](https://virtualenv.pypa.io/en/stable/)
## Tensorflow versions
The master branch supports Tensorflow from version 1.4 to 1.14. For Tensorflow 2.0 support, please use tf2 branch.
## Installation
- Clone the repo and cd into it:
```bash
git clone https://github.com/openai/baselines.git
cd baselines
```
- If you don't have TensorFlow installed already, install your favourite flavor of TensorFlow. In most cases, you may use
```bash
pip install tensorflow-gpu==1.14 # if you have a CUDA-compatible gpu and proper drivers
```
or
```bash
pip install tensorflow==1.14
```
to install Tensorflow 1.14, which is the latest version of Tensorflow supported by the master branch. Refer to [TensorFlow installation guide](https://www.tensorflow.org/install/)
for more details.
- Install baselines package
```bash
pip install -e .
```
### MuJoCo
Some of the baselines examples use [MuJoCo](http://www.mujoco.org) (multi-joint dynamics in contact) physics simulator, which is proprietary and requires binaries and a license (temporary 30-day license can be obtained from [www.mujoco.org](http://www.mujoco.org)). Instructions on setting up MuJoCo can be found [here](https://github.com/openai/mujoco-py)
## Testing the installation
All unit tests in baselines can be run using pytest runner:
```
pip install pytest
pytest
```
## Training models
Most of the algorithms in baselines repo are used as follows:
```bash
python -m baselines.run --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
```bash
python -m baselines.run --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:
```bash
python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --network=mExcerpt of 8,617 characters
Read on GitHub
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John Schulman · United States
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Andrew · University of Toronto · Canada
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Szymon Sidor · OpenAI · United States
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Christopher Hesse · OpenAI · United States
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Xingdong Zuo · NAVER Corp · South Korea
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Matthias Plappert · Germany
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Hong Kong
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Matthew Rahtz
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:29d39e22e9b576c3, desc:reinforcement learning