Pre-release
This release is a pre-release and may not be stable for production use.
# 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.
The documentation, examples and tutorials will grow over the next few weeks.
## Table of contents
<a href="#Agents">Agents</a><br>
<a href="#Tutorials">Tutorials</a><br>
<a href='#Examples'>Examples</a><br>
<a href="#Installation">Installation</a><br>
<a href='#Contributing'>Contributing</a><br>
<a href='#Principles'>Principles</a><br>
<a href='#Citation'>Citation</a><br>
<a href='#Disclaimer'>Disclaimer</a><br>
<a id='Agents'></a>
## 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:
* [DQN: __Human level control through deep reinforcement learning__ Mnih et al., 2015](https://deepmind.com/research/dqn/)
* [DDQN: __Deep Reinforcement Learning with Double Q-learning__ Hasselt et al., 2015](https://arxiv.org/abs/1509.06461)
* [DDPG: __Continuous control with deep reinforcement learning__ Lillicrap et al., 2015](https://arxiv.org/abs/1509.02971)
* [TD3: __Addressing Function Approximation Error in Actor-Critic Methods__ Fujimoto et al., 2018](https://arxiv.org/abs/1802.09477)
* [REINFORCE: __Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning__ Williams, 1992](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
* [PPO: __Proximal Policy Optimization Algorithms__ Schulman et al., 2017](https://arxiv.org/abs/1707.06347)
* [SAC: __Soft Actor Critic__ Haarnoja et al., 2018](https://arxiv.org/abs/1812.05905)
<a id='Tutorials'></a>
## Tutorials
See [`tf_agents/colabs/`](https://github.com/tensorflow/agents/tree/master/tf_agents/colabs/)
for tutorials on the major components provided.
<a id='Examples'></a>
## Examples
End-to-end examples training agents can be found under each agent directory.
e.g.:
* DQN: [`tf_agents/agents/dqn/examples/v1/train_eval_gym.py`](https://github.com/tensorflow/agents/tree/master/tf_agents/agents/dqn/examples/v1/train_eval_gym.py)
<a id='Installation'></a>
## 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:
```shell
# 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-nighly` installation. You can then run `pip install -e .[tests]` from the agents directory to get dependencies to run tests.
<a id='Contributing'></a>
## Contributing
We're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md)
for a guide on how to contribute. This project adheres to TensorFlow's
[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to
uphold this code.
<a id='Principles'></a>
## Principles
This project adheres to [Google's AI principles](PRINCIPLES.md).
By participating, using or contributing to this project you are expected to
adhere to these principles.
<a id='Citation'></a>
## 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,
Chris Harris, Vincent Vanhoucke, Eugene Brevdo}",
howpublished = {\url{https://github.com/tensorflow/agents}},
url = "https://github.com/tensorflow/agents",
year = 2018,
note = "[Online; accessed 30-November-2018]"
}
```
<a id='Disclaimer'></a>
## Disclaimer
This is not an official Google product.
*NOTE:* Current TF-Agents pre-release is under active development and
interfaces may change at any time. Feel free to provide feedback and comments.
The documentation, examples and tutorials will grow over the next few weeks.
## Table of contents
<a href="#Agents">Agents</a><br>
<a href="#Tutorials">Tutorials</a><br>
<a href='#Examples'>Examples</a><br>
<a href="#Installation">Installation</a><br>
<a href='#Contributing'>Contributing</a><br>
<a href='#Principles'>Principles</a><br>
<a href='#Citation'>Citation</a><br>
<a href='#Disclaimer'>Disclaimer</a><br>
<a id='Agents'></a>
## 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:
* [DQN: __Human level control through deep reinforcement learning__ Mnih et al., 2015](https://deepmind.com/research/dqn/)
* [DDQN: __Deep Reinforcement Learning with Double Q-learning__ Hasselt et al., 2015](https://arxiv.org/abs/1509.06461)
* [DDPG: __Continuous control with deep reinforcement learning__ Lillicrap et al., 2015](https://arxiv.org/abs/1509.02971)
* [TD3: __Addressing Function Approximation Error in Actor-Critic Methods__ Fujimoto et al., 2018](https://arxiv.org/abs/1802.09477)
* [REINFORCE: __Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning__ Williams, 1992](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)
* [PPO: __Proximal Policy Optimization Algorithms__ Schulman et al., 2017](https://arxiv.org/abs/1707.06347)
* [SAC: __Soft Actor Critic__ Haarnoja et al., 2018](https://arxiv.org/abs/1812.05905)
<a id='Tutorials'></a>
## Tutorials
See [`tf_agents/colabs/`](https://github.com/tensorflow/agents/tree/master/tf_agents/colabs/)
for tutorials on the major components provided.
<a id='Examples'></a>
## Examples
End-to-end examples training agents can be found under each agent directory.
e.g.:
* DQN: [`tf_agents/agents/dqn/examples/v1/train_eval_gym.py`](https://github.com/tensorflow/agents/tree/master/tf_agents/agents/dqn/examples/v1/train_eval_gym.py)
<a id='Installation'></a>
## 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:
```shell
# 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-nighly` installation. You can then run `pip install -e .[tests]` from the agents directory to get dependencies to run tests.
<a id='Contributing'></a>
## Contributing
We're eager to collaborate with you! See [`CONTRIBUTING.md`](CONTRIBUTING.md)
for a guide on how to contribute. This project adheres to TensorFlow's
[code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to
uphold this code.
<a id='Principles'></a>
## Principles
This project adheres to [Google's AI principles](PRINCIPLES.md).
By participating, using or contributing to this project you are expected to
adhere to these principles.
<a id='Citation'></a>
## 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,
Chris Harris, Vincent Vanhoucke, Eugene Brevdo}",
howpublished = {\url{https://github.com/tensorflow/agents}},
url = "https://github.com/tensorflow/agents",
year = 2018,
note = "[Online; accessed 30-November-2018]"
}
```
<a id='Disclaimer'></a>
## Disclaimer
This is not an official Google product.
4 maintainers
Metadata
4 maintainers
Release files for tf-agents-nightly 0.2.0.dev20190321
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tf_agents_nightly-0.2.0.dev20190321-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Release files / tf_agents_nightly-0.2.0.dev20190321-py2.py3-none-any.whl
| Download URL | tf_agents_nightly-0.2.0.dev20190321-py2.py3-none-any.whl |
|---|---|
| Size | 501.0 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
0cf90ed4933542530a889b2324b4b90f60fe5ea311d8c8b7eaf27f6c1154de05
|
|
BLAKE2b-256 checksum How to use checksums |
ad3f9ec75ef319753b21d0f4fb0b1b99082b81113601146cbc7fbab31b106d2b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.1
|
Security for tf-agents-nightly 0.2.0.dev20190321
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
