tensorflow

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
Under Apache License 2.0
By tensorflow

tensorflow reinforcement-learning dqn contextual-bandits bandits multi-armed-bandits tf-agents rl-algorithms

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.


TF-Agents makes implementing, deploying,
and testing new Bandits and RL algorithms easier. It provides well tested and
modular components that can be modified and extended. It enables fast code
iteration, with good test integration and benchmarking.


To get started, we recommend checking out one of our Colab tutorials. If you
need an intro to RL (or a quick recap),
start here. Otherwise, check out our
DQN tutorial to get an agent up and
running in the Cartpole environment. API documentation for the current stable
release is on
tensorflow.org.


TF-Agents is under active development and interfaces may change at any time.
Feedback and comments are welcome.


Table of contents

Agents
Tutorials
Multi-Armed Bandits
Examples
Installation
Contributing
Releases
Principles
Contributors
Citation
Disclaimer



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 experience.


Currently the following algorithms are available under TF-Agents:




Tutorials

See docs/tutorials/ for tutorials on the major components
provided.



Multi-Armed Bandits

The TF-Agents library contains a comprehensive Multi-Armed Bandits suite,
including Bandits environments and agents. RL agents can also be used on Bandit
environments. There is a tutorial in
bandits_tutorial.ipynb.
and ready-to-run examples in
tf_agents/bandits/agents/examples/v2.



Examples

End-to-end examples training agents can be found under each agent directory.
e.g.:




Installation

TF-Agents publishes nightly and stable builds. For a list of releases read the
Releases section. The commands below cover installing
TF-Agents stable and nightly from pypi.org as well as from a
GitHub clone.


Stable

Run the commands below to install the most recent stable release. API
documentation for the release is on
tensorflow.org.


```shell
$ pip install --user tf-agents[reverb]


Use this tag get the matching examples and colabs.

$ git clone https://github.com/tensorflow/agents.git
$ cd agents
$ git checkout v0.9.0
```


If you want to install TF-Agents with versions of Tensorflow or
Reverb that are flagged as not compatible
by the pip dependency check, use the following pattern below at your own risk.


shell
$ pip install --user tensorflow
$ pip install --user dm-reverb
$ pip install --user tf-agents


If you want to use TF-Agents with TensorFlow 1.15 or 2.0, install version 0.3.0:


```shell


Newer versions of tensorflow-probability require newer versions of TensorFlow.

$ pip install tensorflow-probability==0.8.0
$ pip install tf-agents==0.3.0
```


Nightly

Nightly builds include newer features, but may be less stable than the versioned
releases. The nightly build is pushed as tf-agents-nightly. We suggest
installing nightly versions of TensorFlow (tf-nightly) and TensorFlow
Probability (tfp-nightly) as those are the versions TF-Agents nightly are
tested against.


To install the nightly build version, run the following:


```shell


`--force-reinstall helps guarantee the right versions.

$ pip install --user --force-reinstall tf-nightly
$ pip install --user --force-reinstall tfp-nightly
$ pip install --user --force-reinstall dm-reverb-nightly


Installing with the --upgrade flag ensures you'll get the latest version.

$ pip install --user --upgrade tf-agents-nightly
```


From GitHub

After cloning the repository, the dependencies can be installed by running pip
install -e .[tests]. TensorFlow needs to be installed independently: pip
install --user tf-nightly.



Contributing

We're eager to collaborate with you! See CONTRIBUTING.md
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.



Releases

TF Agents has stable and nightly releases. The nightly releases are often fine
but can have issues due to upstream libraries being in flux. The table below
lists the version(s) of TensorFlow tested with each TF Agents' release to help
users that may be locked into a specific version of TensorFlow. 0.9.0 was the
last release compatible with Python 3.6. 0.3.0 was the last release compatible
with Python 2.


Release | Branch / Tag | TensorFlow Version
------- | ---------------------------------------------------------- | ------------------
Nightly | master | tf-nightly
0.9.0 | v0.9.0 | 2.6.0
0.8.0 | v0.8.0 | 2.5.0
0.7.1 | v0.7.1 | 2.4.0
0.6.0 | v0.6.0 | 2.3.0
0.5.0 | v0.5.0 | 2.2.0
0.4.0 | v0.4.0 | 2.1.0
0.3.0 | v0.3.0 | 1.15.0 and 2.0.0



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.



Contributors

We would like to recognize the following individuals for their code
contributions, discussions, and other work to make the TF-Agents library.




Citation

If you use this code, please cite it as:


@misc{TFAgents,
title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow},
author = {Sergio Guadarrama and Anoop Korattikara and Oscar Ramirez and
Pablo Castro and Ethan Holly and Sam Fishman and Ke Wang and
Ekaterina Gonina and Neal Wu and Efi Kokiopoulou and Luciano Sbaiz and
Jamie Smith and Gábor Bartók and Jesse Berent and Chris Harris and
Vincent Vanhoucke and Eugene Brevdo},
howpublished = {\url{https://github.com/tensorflow/agents}},
url = "https://github.com/tensorflow/agents",
year = 2018,
note = "[Online; accessed 25-June-2019]"
}



Disclaimer

This is not an official Google product.