Skip to main content

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.

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.

Table of contents

Agents
Tutorials
Multi-Armed Bandits
Examples
Installation
Contributing
Principles
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 tf_agents/colabs/ 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/.

Examples

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

Installation

To install the latest version, use 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 and also tfp-nightly. 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 tf-nightly

If you clone the repository you will still need a tf-nightly installation. You can then run pip install -e .[tests] from the agents directory to get dependencies to run tests.

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.

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, 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{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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

tf_agents_nightly-0.2.0.dev20191109-py2.py3-none-any.whl (794.0 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file tf_agents_nightly-0.2.0.dev20191109-py2.py3-none-any.whl.

File metadata

  • Download URL: tf_agents_nightly-0.2.0.dev20191109-py2.py3-none-any.whl
  • Upload date:
  • Size: 794.0 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.38.0 CPython/3.6.1

File hashes

Hashes for tf_agents_nightly-0.2.0.dev20191109-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 6d00356d199fd5f30d53bf0301d77668f30f995e18d8cecc0ea91ccfec6d313a
MD5 25459b0aa646e7df9263cfa3fa3c7373
BLAKE2b-256 5e43733bf177fda194dcd2a1c5460994e3bb5ecb5792e1815aab52e5a621ce27

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page