overview.md

TensorFlow Agents

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.

⚠️ If using Reverb (replay buffer), which is very common, TF-Agents will only work with Linux.

Note: Python 3.11 requires pygame 2.1.3+.

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 this tag get the matching examples and colabs.
$ git clone https://github.com/tensorflow/agents.git
$ cd agents
$ git checkout v0.17.0

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 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:

# `--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 that align with each TF Agents' release. Release versions of interest:

• 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
0.17.0	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]"
}