<commit_before>*TensorForce - modular deep reinforcement learning in TensorFlow*
=================================================================================

.. |logo1| image:: https://badges.gitter.im/reinforceio/TensorForce.svg
           :scale: 100%
           :target: https://gitter.im/reinforceio/TensorForce?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
           :align: top
.. |logo2| image:: https://travis-ci.org/reinforceio/tensorforce.svg?branch=master
   :scale: 50%
   :align: top

+---------+---------+
| |logo1| | |logo2| |
+---------+---------+


Introduction
------------

TensorForce is an open source reinforcement learning library focused on
providing clear APIs, readability and modularisation to deploy
reinforcement learning solutions both in research and practice.
TensorForce is built on top of TensorFlow and compatible with Python 2.7 and >3.5.

The main difference to existing libraries is a strict
separation of environments, agents and update logic that facilitates
usage in non-simulation environments. Further, research code often relies on fixed network
architectures that have been used to tackle particular benchmarks.
TensorForce is built with the idea that (almost) everything should be
optionally configurable and in particular uses value function template
configurations to be able to quickly experiment with new models. The
goal of TensorForce is to provide a practitioner's reinforcement
learning framework that integrates into modern software service
architectures.

TensorForce is actively being maintained and developed both to
continuously improve the existing code as well as to reflect new
developments as they arise (see road map for more). The aim is not to
include every new trick but to adopt methods as
they prove themselves stable, e.g. as of early 2017 hybrid A3C and TRPO
variants provide the basis for a lot of research. We also offer TensorForce
support through our Gitter channel.

Features
--------

TensorForce currently integrates with the OpenAI Gym API, OpenAI
Universe and DeepMind lab. The following algorithms are available (all policy methods both continuous/discrete):

1. A3C using distributed TensorFlow
2. Trust Region Policy Optimization (TRPO) with generalised
   advantage estimation (GAE)
3. Normalised Advantage functions (NAFs)
4. DQN/Double-DQN
5. Vanilla Policy Gradients (VPG)
6. Deep Q-learning from Demonstration (DQFD) - `paper <https://arxiv.org/abs/1704.03732>`__

Installation
------------

For the most straight-forward install via pip, execute:

::

    git clone git@github.com:reinforceio/tensorforce.git
    cd tensorforce
    pip install -e .

To update TensorForce, just run ``git pull`` in the tensorforce
directory. Please note that we did not include OpenAI Gym/Universe/DeepMind lab in the default
install script because not everyone will want to use these. Please install them as required,
usually via pip.


Docker coming soon.

Examples and documentation
--------------------------

For a quick start, you can run one of our example scripts using the
provided configurations, e.g. to run the TRPO agent on CartPole, execute
from the examples folder:

::

    python tensorforce/examples/openai_gym.py CartPole-v0 -a TRPOAgent -c tensorforce/examples/configs/trpo_cartpole.json
    -n tensorforce/examples/configs/trpo_network_example.json

Documentation is available at `ReadTheDocs <http://tensorforce.readthedocs.io>`__. We also have tests validating models
on minimal environments which can be run from the main directory by executing :code:`pytest`.

Use with DeepMind lab<commit_msg>little readme fix<commit_after>*TensorForce - modular deep reinforcement learning in TensorFlow*
=================================================================================

.. |logo1| image:: https://badges.gitter.im/reinforceio/TensorForce.svg
           :scale: 100%
           :target: https://gitter.im/reinforceio/TensorForce?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
           :align: top
.. |logo2| image:: https://travis-ci.org/reinforceio/tensorforce.svg?branch=master
   :scale: 50%
   :align: top

+---------+---------+
| |logo1| | |logo2| |
+---------+---------+


Introduction
------------

TensorForce is an open source reinforcement learning library focused on
providing clear APIs, readability and modularisation to deploy
reinforcement learning solutions both in research and practice.
TensorForce is built on top of TensorFlow and compatible with Python 2.7 and >3.5.

The main difference to existing libraries is a strict
separation of environments, agents and update logic that facilitates
usage in non-simulation environments. Further, research code often relies on fixed network
architectures that have been used to tackle particular benchmarks.
TensorForce is built with the idea that (almost) everything should be
optionally configurable and in particular uses value function template
configurations to be able to quickly experiment with new models. The
goal of TensorForce is to provide a practitioner's reinforcement
learning framework that integrates into modern software service
architectures.

TensorForce is actively being maintained and developed both to
continuously improve the existing code as well as to reflect new
developments as they arise (see road map for more). The aim is not to
include every new trick but to adopt methods as
they prove themselves stable, e.g. as of early 2017 hybrid A3C and TRPO
variants provide the basis for a lot of research. We also offer TensorForce
support through our Gitter channel.

Features
--------

TensorForce currently integrates with the OpenAI Gym API, OpenAI
Universe and DeepMind lab. The following algorithms are available (all policy methods both continuous/discrete):

1. A3C using distributed TensorFlow
2. Trust Region Policy Optimization (TRPO) with generalised
   advantage estimation (GAE)
3. Normalised Advantage functions (NAFs)
4. DQN/Double-DQN
5. Vanilla Policy Gradients (VPG)
6. Deep Q-learning from Demonstration (DQFD) - `paper <https://arxiv.org/abs/1704.03732>`__

Installation
------------

For the most straight-forward install via pip, execute:

::

    git clone <EMAIL>:reinforceio/tensorforce.git
    cd tensorforce
    pip install -e .

To update TensorForce, just run ``git pull`` in the tensorforce
directory. Please note that we did not include OpenAI Gym/Universe/DeepMind lab in the default
install script because not everyone will want to use these. Please install them as required,
usually via pip.


Docker coming soon.

Examples and documentation
--------------------------

For a quick start, you can run one of our example scripts using the
provided configurations, e.g. to run the TRPO agent on CartPole, execute
from the examples folder:

::

    python tensorforce/examples/openai_gym.py CartPole-v0 -a TRPOAgent -c tensorforce/examples/configs/trpo_cartpole.json
    -n tensorforce/examples/configs/trpo_cartpole_network.json

Documentation is available at `ReadTheDocs <http://tensorforce.readthedocs.io>`__. We also have tests validating models
on minimal environments which can be run from the main directory by executing :code:`pytest`.

Use with DeepMind lab
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