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Coach is a python reinforcement learning framework containing implementation of many state-of-the-art algorithms.

It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless.

Training an agent to solve an environment is as easy as running:

coach -p CartPole_DQN -r

Fetch Slide Pendulum Starcraft
Doom Deathmatch CARLA MontezumaRevenge
Doom Health Gathering PyBullet Minitaur Gym Extensions Ant

Table of Contents


One of the main challenges when building a research project, or a solution based on a published algorithm, is getting a concrete and reliable baseline that reproduces the algorithm's results, as reported by its authors. To address this problem, we are releasing a set of benchmarks that shows Coach reliably reproduces many state of the art algorithm results.


Note: Coach has only been tested on Ubuntu 16.04 LTS, and with Python 3.5.

For some information on installing on Ubuntu 17.10 with Python 3.6.3, please refer to the following issue:

In order to install coach, there are a few prerequisites required. This will setup all the basics needed to get the user going with running Coach on top of OpenAI Gym environments:

# General
sudo -E apt-get install python3-pip cmake zlib1g-dev python3-tk python-opencv -y

# Boost libraries
sudo -E apt-get install libboost-all-dev -y

# Scipy requirements
sudo -E apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran -y

# PyGame
sudo -E apt-get install libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev
libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev -y

# Dashboard
sudo -E apt-get install dpkg-dev build-essential python3.5-dev libjpeg-dev  libtiff-dev libsdl1.2-dev libnotify-dev 
freeglut3 freeglut3-dev libsm-dev libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev libwebkitgtk-3.0-dev
libgstreamer-plugins-base1.0-dev -y

# Gym
sudo -E apt-get install libav-tools libsdl2-dev swig cmake -y

We recommend installing coach in a virtualenv:

sudo -E pip3 install virtualenv
virtualenv -p python3 coach_env
. coach_env/bin/activate

Finally, install coach using pip:

pip3 install rl_coach

Or alternatively, for a development environment, install coach from the cloned repository:

cd coach
pip3 install -e .

If a GPU is present, Coach's pip package will install tensorflow-gpu, by default. If a GPU is not present, an Intel-Optimized TensorFlow, will be installed.

In addition to OpenAI Gym, several other environments were tested and are supported. Please follow the instructions in the Supported Environments section below in order to install more environments.

Getting Started

Tutorials and Documentation

Jupyter notebooks demonstrating how to run Coach from command line or as a library, implement an algorithm, or integrate an environment.

Framework documentation, algorithm description and instructions on how to contribute a new agent/environment.

Basic Usage

Running Coach

To allow reproducing results in Coach, we defined a mechanism called preset. There are several available presets under the presets directory. To list all the available presets use the -l flag.

To run a preset, use:

coach -r -p <preset_name>

For example:

  • CartPole environment using Policy Gradients (PG):

    coach -r -p CartPole_PG
  • Basic level of Doom using Dueling network and Double DQN (DDQN) algorithm:

    coach -r -p Doom_Basic_Dueling_DDQN

Some presets apply to a group of environment levels, like the entire Atari or Mujoco suites for example. To use these presets, the requeseted level should be defined using the -lvl flag.

For example:

  • Pong using the Neural Episodic Control (NEC) algorithm:

    coach -r -p Atari_NEC -lvl pong

There are several types of agents that can benefit from running them in a distributed fashion with multiple workers in parallel. Each worker interacts with its own copy of the environment but updates a shared network, which improves the data collection speed and the stability of the learning process. To specify the number of workers to run, use the -n flag.

For example:

  • Breakout using Asynchronous Advantage Actor-Critic (A3C) with 8 workers:

    coach -r -p Atari_A3C -lvl breakout -n 8

It is easy to create new presets for different levels or environments by following the same pattern as in

More usage examples can be found here.

Running Coach Dashboard (Visualization)

Training an agent to solve an environment can be tricky, at times.

In order to debug the training process, Coach outputs several signals, per trained algorithm, in order to track algorithmic performance.

While Coach trains an agent, a csv file containing the relevant training signals will be saved to the 'experiments' directory. Coach's dashboard can then be used to dynamically visualize the training signals, and track algorithmic behavior.

To use it, run:

Coach Design

Distributed Multi-Node Coach

As of release 0.11.0, Coach supports horizontal scaling for training RL agents on multiple nodes. In release 0.11.0 this was tested on the ClippedPPO and DQN agents. For usage instructions please refer to the documentation here.

Batch Reinforcement Learning

Training and evaluating an agent from a dataset of experience, where no simulator is available, is supported in Coach. There are example presets and a tutorial.

Supported Environments

Note on MuJoCo version

OpenAI Gym supports MuJoCo only up to version 1.5 (and corresponding mujoco-py version 1.50.x.x). The Robosuite simulation framework, however, requires MuJoCo version 2.0 (and corresponding mujoco-py version, as of robosuite version 1.2). Therefore, if you wish to run both Gym-based MuJoCo environments and Robosuite environments, it's recommended to have a separate virtual environment for each.

Please note that all Gym-Based MuJoCo presets in Coach (rl_coach/presets/Mujoco_*.py) have been validated only with MuJoCo 1.5 (including the reported benchmark results).

Supported Algorithms

Coach Design

Value Optimization Agents

Policy Optimization Agents

General Agents

Imitation Learning Agents

Hierarchical Reinforcement Learning Agents

Memory Types

Exploration Techniques


If you used Coach for your work, please use the following citation:

  author       = {Caspi, Itai and
                  Leibovich, Gal and
                  Novik, Gal and
                  Endrawis, Shadi},
  title        = {Reinforcement Learning Coach},
  month        = dec,
  year         = 2017,
  doi          = {10.5281/zenodo.1134899},
  url          = {}


We'd be happy to get any questions or contributions through GitHub issues and PRs.

Please make sure to take a look here before filing an issue or proposing a PR.

The Coach development team can also be contacted over email


Coach is released as a reference code for research purposes. It is not an official Intel product, and the level of quality and support may not be as expected from an official product. Additional algorithms and environments are planned to be added to the framework. Feedback and contributions from the open source and RL research communities are more than welcome.

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