Coach

Overview

Coach is a python reinforcement learning research 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:

python3 coach.py -p CartPole_DQN -r

Blog posts from the Intel® AI website:

Release 0.8.0 (initial release)
Release 0.9.0

Contacting the Coach development team is also possible through the email coach@intel.com

Coach

Documentation

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

Installation

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

Coach Installer

Coach's installer will setup all the basics needed to get the user going with running Coach on top of OpenAI Gym environments. This can be done by running the following command and then following the on-screen printed instructions:

./install.sh

Coach creates a virtual environment and installs in it to avoid changes to the user's system.

In order to activate and deactivate Coach's virtual environment:

source coach_env/bin/activate

deactivate

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.

TensorFlow GPU Support

Coach's installer installs Intel-Optimized TensorFlow, which does not support GPU, by default. In order to have Coach running with GPU, a GPU supported TensorFlow version must be installed. This can be done by overriding the TensorFlow version:

pip3 install tensorflow-gpu

Usage

Running Coach

Coach supports both TensorFlow and neon deep learning frameworks.

Switching between TensorFlow and neon backends is possible by using the -f flag.

Using TensorFlow (default): -f tensorflow

Using neon: -f neon

There are several available presets in presets.py. To list all the available presets use the -l flag.

To run a preset, use:

python3 coach.py -r -p <preset_name>

For example:

CartPole environment using Policy Gradients:

python3 coach.py -r -p CartPole_PG

Pendulum using Clipped PPO:

python3 coach.py -r -p Pendulum_ClippedPPO -n 8

MountainCar using A3C:

python3 coach.py -r -p MountainCar_A3C -n 8

Doom basic level using Dueling network and Double DQN algorithm:

 python3 coach.py -r -p Doom_Basic_Dueling_DDQN

Doom health gathering level using Mixed Monte Carlo:

python3 coach.py -r -p Doom_Health_MMC

SenseNet TouchWand using DQN:

python3 coach.py -r -p TouchWand_DQN

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

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:

python3 dashboard.py

Parallelizing an Algorithm

Since the introduction of A3C in 2016, many algorithms were shown to benefit from running multiple instances in parallel, on many CPU cores. So far, these algorithms include A3C, DDPG, PPO, and NAF, and this is most probably only the begining.

Parallelizing an algorithm using Coach is straight-forward.

The following method of NetworkWrapper parallelizes an algorithm seamlessly:

network.train_and_sync_networks(current_states, targets)

Once a parallelized run is started, the train_and_sync_networks API will apply gradients from each local worker's network to the main global network, allowing for parallel training to take place.

Then, it merely requires running Coach with the -n flag and with the number of workers to run with. For instance, the following command will set 16 workers to work together to train a MuJoCo Hopper:

python3 coach.py -p Hopper_A3C -n 16

Supported Environments

OpenAI Gym:

Installed by default by Coach's installer.
ViZDoom:

Follow the instructions described in the ViZDoom repository -

https://github.com/mwydmuch/ViZDoom

Additionally, Coach assumes that the environment variable VIZDOOM_ROOT points to the ViZDoom installation directory.
Roboschool:

Follow the instructions described in the roboschool repository -

https://github.com/openai/roboschool
GymExtensions:

Follow the instructions described in the GymExtensions repository -

https://github.com/Breakend/gym-extensions

Additionally, add the installation directory to the PYTHONPATH environment variable.
PyBullet:

Follow the instructions described in the Quick Start Guide (basically just - 'pip install pybullet')
CARLA:

Download release 0.7 from the CARLA repository -

https://github.com/carla-simulator/carla/releases

Create a new CARLA_ROOT environment variable pointing to CARLA's installation directory.

A simple CARLA settings file (CarlaSettings.ini) is supplied with Coach, and is located in the environments directory.
SenseNet:

Follow the instructions described in the Sensenet repository -

https://github.com/jtoy/sensenet

basically just 'pip install sensenet'

Supported Algorithms

Deep Q Network (DQN) (code)
Double Deep Q Network (DDQN) (code)
Dueling Q Network
Mixed Monte Carlo (MMC) (code)
Persistent Advantage Learning (PAL) (code)
Categorical Deep Q Network (C51) (code)
Quantile Regression Deep Q Network (QR-DQN) (code)
Bootstrapped Deep Q Network (code)
N-Step Q Learning | Distributed (code)
Neural Episodic Control (NEC) (code)
Normalized Advantage Functions (NAF) | Distributed (code)
Policy Gradients (PG) | Distributed (code)
Asynchronous Advantage Actor-Critic (A3C) | Distributed (code)
Deep Deterministic Policy Gradients (DDPG) | Distributed (code)
Proximal Policy Optimization (PPO) (code)
Clipped Proximal Policy Optimization | Distributed (code)
Direct Future Prediction (DFP) | Distributed (code)
Behavioral Cloning (BC) (code)

Citation

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

@misc{caspi_itai_2017_1134899,
  author       = {Caspi, Itai and
                  Leibovich, Gal and
                  Novik, Gal},
  title        = {Reinforcement Learning Coach},
  month        = dec,
  year         = 2017,
  doi          = {10.5281/zenodo.1134899},
  url          = {https://doi.org/10.5281/zenodo.1134899}
}

Disclaimer

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.

jtoy/sensenet-coach