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TF-Agents is a library for Reinforcement Learning in TensorFlow
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TF-Agents: A reliable, scalable and easy to use Reinforcement Learning library for TensorFlow.

TF-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 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

NOTE: 0.5.0 RC0 is now available and was tested with Python3 and TensorFlow 2.2. pip install tf-agents==0.5.0rc0.

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

Table of contents

Multi-Armed Bandits


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:


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

Multi-Armed Bandits

The TF-Agents library contains also a Multi-Armed Bandits suite with a few environments and agents. RL agents can also be used on Bandit environments. For a tutorial, see tf_agents/bandits/colabs/bandits_tutorial.ipynb. For examples ready to run, see tf_agents/bandits/agents/examples/.


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


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 as well as from a GitHub clone.


Run the commands below to install the most recent stable release (0.4.0), which was tested with TensorFlow 2.1.x and and Python3. API documentation for the release is on

pip install --user tf-agents
pip install --user tensorflow==2.1.0

# To get the matching examples and colabs
git clone
cd agents
git checkout v0.4.0

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

pip install tf-agents==0.3.0
# Newer versions of tensorflow-probability require newer versions of TensorFlow.
pip install tensorflow-probability==0.8.0


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 version TF-Agents nightly are tested against. Nightly releases are only compatible with Python 3 as of 17-JAN-2020.

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 tf-nightly
# `--force-reinstall helps guarantee the right version.
pip install --user --force-reinstall tf-nightly
pip install --user --force-reinstall tfp-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.


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.


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. 0.3.0 was the last release compatible with Python 2.

Release Branch / Tag TensorFlow Version
Nightly master tf-nightly
0.4.0 v0.4.0 2.1.0
0.3.0 v0.3.0 1.15.0 and 2.0.0

Examples of installing nightly, most recent stable, and a specific version of TF-Agents:

# Stable
pip install tf-agents

# Nightly
pip install tf-agents-nightly

# Specific version
pip install tf-agents==0.4.0rc0


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


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, Ekaterina Gonina, Neal Wu,
    Efi Kokiopoulou, Luciano Sbaiz, Jamie Smith, Gábor Bartók, Jesse Berent,
    Chris Harris, Vincent Vanhoucke, Eugene Brevdo}",
  howpublished = {\url{}},
  url = "",
  year = 2018,
  note = "[Online; accessed 25-June-2019]"


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