Reinforcement Learning with TensorFlow
Agents makes designing, implementing and testing new RL algorithms easier, by providing well tested 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 tutorials.
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.
:warning: If using Reverb (replay buffer), which is very common, TF-Agents will only work with Linux.
Stable
Run the commands below to install the most recent stable release. API documentation for the release is on tensorflow.org.
$ pip install --user tf-agents[reverb]
# Use keras-2
$ export TF_USE_LEGACY_KERAS=1
# Use this tag get the matching examples and colabs.
$ git clone https://github.com/tensorflow/agents.git
$ cd agents
$ git checkout v0.19.0
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.
$ pip install --user tensorflow
$ pip install --user tf-keras
$ 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:
# 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:
# Use keras-2
$ export TF_USE_LEGACY_KERAS=1
# `--force-reinstall helps guarantee the right versions.
$ pip install --user --force-reinstall tf-nightly
$ pip install --user --force-reinstall tf-keras-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 that align with each TF Agents' release. Release versions of interest:
- 0.19.0 supports tensorflow-2.15.0.
- 0.18.0 dropped Python 3.8 support.
- 0.16.0 is the first version to support Python 3.11.
- 0.15.0 is the last release compatible with Python 3.7.
- If using numpy < 1.19, then use TF-Agents 0.15.0 or earlier.
- 0.9.0 is the last release compatible with Python 3.6.
- 0.3.0 is the last release compatible with Python 2.x.
Release | Branch / Tag | TensorFlow Version | dm-reverb Version |
---|---|---|---|
Nightly | master | tf-nightly | dm-reverb-nightly |
0.19.0 | v0.19.0 | 2.15.0 | 0.14.0 |
0.18.0 | v0.18.0 | 2.14.0 | 0.13.0 |
0.17.0 | v0.17.0 | 2.13.0 | 0.12.0 |
0.16.0 | v0.16.0 | 2.12.0 | 0.11.0 |
0.15.0 | v0.15.0 | 2.11.0 | 0.10.0 |
0.14.0 | v0.14.0 | 2.10.0 | 0.9.0 |
0.13.0 | v0.13.0 | 2.9.0 | 0.8.0 |
0.12.0 | v0.12.0 | 2.8.0 | 0.7.0 |
0.11.0 | v0.11.0 | 2.7.0 | 0.6.0 |
0.10.0 | v0.10.0 | 2.6.0 | |
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.
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]"
}