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TF-Agents: A Reinforcement Learning Library for TensorFlow

Project description

# TF-Agents: A library for Reinforcement Learning in TensorFlow

*NOTE:* Current TF-Agents pre-release is under active development and
interfaces may change at any time. Feel free to provide feedback and comments.

The documentation, examples and tutorials will grow over the next few weeks.

## Table of contents

<a href="#Agents">Agents</a><br>
<a href="#Tutorials">Tutorials</a><br>
<a href='#Examples'>Examples</a><br>
<a href="#Installation">Installation</a><br>
<a href='#Contributing'>Contributing</a><br>
<a href='#Principles'>Principles</a><br>
<a href='#Citation'>Citation</a><br>
<a href='#Disclaimer'>Disclaimer</a><br>

<a id='Agents'></a>
## Agents

In TF-Agents, the core elements of RL algorithms are implemented as `Agents`.
An agent encompasses two main responsibilities: defining a Policy to interact
with the Environment, and how to learn/train that Policy from collected

Currently the following algorithms are available under TF-Agents:

* DQN: __Human level control through deep reinforcement learning__ Mnih et al., 2015
* DDQN: __Deep Reinforcement Learning with Double Q-learning__ Hasselt et al., 2015
* DDPG: __Continuous control with deep reinforcement learning__ Lilicrap et al.
* TD3: __Addressing Function Approximation Error in Actor-Critic Methods__ Fujimoto et al.
* REINFORCE: __Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning__ Williams
* PPO: __Proximal Policy Optimization Algorithms__ Schulman et al.

<a id='Tutorials'></a>
## Tutorials

See [`tf_agents/colabs/`](
for tutorials on the major components provided.

<a id='Examples'></a>
## Examples
End-to-end examples training agents can be found under each agent directory.

* DQN: [`tf_agents/agents/dqn/examples/`](

<a id='Installation'></a>
## Installation

### Stable Builds

To install the latest version, run the following:

# Installing with the `--upgrade` flag ensures you'll get the latest version.
pip install --user --upgrade tf-agents # depends on TensorFlow

TF-Agents depends on a recent stable release of
[TensorFlow]( (pip package `tensorflow`).

Note: Since TensorFlow is *not* included as a dependency of the TF-Agents
package (in ``), you must explicitly install the TensorFlow
package (`tensorflow` or `tensorflow-gpu`). This allows us to maintain one
package instead of separate packages for CPU and GPU-enabled TensorFlow.

To force a Python 3-specific install, replace `pip` with `pip3` in the above
commands. For additional installation help, guidance installing prerequisites,
and (optionally) setting up virtual environments, see the [TensorFlow
installation guide](

### Nightly Builds

There are also nightly builds of TF-Agents under the pip package
`tf-agents-nightly`, which requires you install on one of `tf-nightly` and
`tf-nightly-gpu`. Nightly builds include newer features, but may be less stable
than the versioned releases.

To install the nightly build version, run the following:

# Installing with the `--upgrade` flag ensures you'll get the latest version.
pip install --user --upgrade tf-agents-nightly # depends on TensorFlow

<a id='Contributing'></a>
## Contributing

We're eager to collaborate with you! See [``](
for a guide on how to contribute. This project adheres to TensorFlow's
[code of conduct]( By participating, you are expected to
uphold this code.

<a id='Principles'></a>
## Principles

This project adheres to [Google's AI principles](
By participating, using or contributing to this project you are expected to
adhere to these principles.

<a id='Citation'></a>
## Citation

If you use this code please cite it as:

title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow},
author = "{Sergio Guadarrama, Anoop Korattikara, Oscar Ramirez,
Pablo Castro, Ethan Holly, Sam Fishman, Ke Wang, Katya Gonina,
Chris Harris, Vincent Vanhoucke, Eugene Brevdo}",
howpublished = {\url{}},
url = "",
year = 2018,
note = "[Online; accessed 30-November-2018]"

<a id='Disclaimer'></a>
## Disclaimer

This is not an official Google product.

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