Python Library for Autonomous Exploration
# Explauto: A library to study, model and simulate curiosity-driven learning and exploration in virtual and robotic agents #
Explauto is a framework developed in the [Inria FLOWERS](https://flowers.inria.fr/) research team which provides a common interface for the implementation of active sensorimotor learning algorithm.
Explauto provides a high-level API for an easy definition of:
- Virtual and robotics setups (Environment level)
- Sensorimotor learning iterative models (Sensorimotor level)
- Active choice of sensorimotor experiments (Interest level)
It is crossed-platform and has been tested on Linux, Windows and Mac OS. Do not hesitate to contact us if you want to get involved! It has been released under the [GPLv3 license](http://www.gnu.org/copyleft/gpl.html).
## Documentation ##
### Scientific grounding ###
Explauto’s scientific roots trace back from Intelligent Adaptive Curiosity algorithmic architecture [[Oudeyer, 2007]](http://hal.inria.fr/hal-00793610/en), which has been extended to a more general family of autonomous exploration architecture by [[Baranes, 2013]](http://www.pyoudeyer.com/ActiveGoalExploration-RAS-2013.pdf) and recently expressed as a compact and unified formalism [[Moulin-Frier, 2013]](http://hal.inria.fr/hal-00860641). We strongly recommend to read this [short introduction](http://flowersteam.github.io/explauto/about.html) into developmental robotics before going through the tutorials.
### Tutorials ###
Most of Explauto’s documentation is written as [IPython notebooks](http://ipython.org/notebook.html). If you do not know how to use them, please refer to the [dedicated section](http://flowersteam.github.io/explauto/notebook.html).
- [Setting environments](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/setting_environments.ipynb)
- [Learning sensorimotor models](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/learning_sensorimotor_models.ipynb)
- Comming soon: Autonomous exploration using interest models
- [Setting a basic experiment](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/setting_basic_experiment.ipynb)
- [Comparing motor vs goal strategies](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/comparing_motor_goal_stategies.ipynb)
- [Running pool of experiments](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/running_experiment_pool.ipynb)
- [Introducing curiosity-driven exploration](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/introducing_curiosity_learning.ipynb)
- [Using a simulated n-pendulum](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/plotting_npendulum.ipynb)
- [Poppy environment](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/poppy_environment.ipynb)
### API ###
Explauto’s API can be found on a html format [here](http://flowersteam.github.io/explauto/).
## Installation ##
The best way to install Explauto at the moment is to clone the repo and use it in [development mode](http://flowersteam.github.io/explauto/installation.html#as-a-developer). It is also available as a [python package](https://pypi.python.org/pypi/explauto/). The core of explauto depends on the following packages:
- [python](http://www.python.org) 2.7 or 3.*
For more details, please refer to the [installation section](http://flowersteam.github.io/explauto/installation.html) of the documentation.