Metadata-Version: 2.1
Name: Tensorforce
Version: 0.6.4
Summary: Tensorforce: a TensorFlow library for applied reinforcement learning
Home-page: http://github.com/tensorforce/tensorforce
Author: Alexander Kuhnle
Author-email: tensorforce.team@gmail.com
License: Apache 2.0
Download-URL: https://github.com/tensorforce/tensorforce/archive/0.6.4.tar.gz
Platform: UNKNOWN
Classifier: Natural Language :: English
Classifier: Topic :: Scientific/Engineering
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: tfa
Provides-Extra: tune
Provides-Extra: envs
Provides-Extra: ale
Provides-Extra: gym
Provides-Extra: retro
Provides-Extra: vizdoom
Provides-Extra: carla
Provides-Extra: docs
Provides-Extra: tests
License-File: LICENSE

# Tensorforce: a TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework, with an emphasis on modularized flexible library design and straightforward usability for applications in research and practice. Tensorforce is built on top of [Google's TensorFlow framework](https://www.tensorflow.org/) and requires Python 3.

Tensorforce follows a set of high-level design choices which differentiate it from other similar libraries:

- **Modular component-based design**: Feature implementations, above all, strive to be as generally applicable and configurable as possible, potentially at some cost of faithfully resembling details of the introducing paper.
- **Separation of RL algorithm and application**: Algorithms are agnostic to the type and structure of inputs (states/observations) and outputs (actions/decisions), as well as the interaction with the application environment.
- **Full-on TensorFlow models**: The entire reinforcement learning logic, including control flow, is implemented in TensorFlow, to enable portable computation graphs independent of application programming language, and to facilitate the deployment of models.

For more information, see the [GitHub project page](https://github.com/tensorforce/tensorforce) and [ReadTheDocs documentation](https://tensorforce.readthedocs.io/en/latest/).


