explauto 1.3.0

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 and online sensorimotor learning algorithms. It is designed and maintained by [Clément Moulin-Frier](https://flowers.inria.fr/clement_mf/), [Pierre Rouanet](https://github.com/pierre-rouanet), and [Sébastien Forestier](http://sforestier.com/).

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.

If you use the library in a scientific paper, please cite (follow the link for bibtex and pdf files):

Moulin-Frier, C.; Rouanet, P. & Oudeyer, P.-Y. [Explauto: an open-source Python library to study autonomous exploration in developmental robotics](http://hal.inria.fr/hal-01061708) International Conference on Development and Learning, ICDL/Epirob, Genova, Italy, 2014

### 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).

• [Full tutorial describing how to use the library](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/full_tutorial.ipynb)

• More specific tutorials

  • [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)

  • [Learning sensorimotor models with sensorimotor context](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/learning_with_sensorimotor_context.ipynb)

  • [Learning sensorimotor models with context provided by environment](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/learning_with_environment_context.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)

  • [Poppy environment](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/poppy_environment.ipynb)

  • [Fast-forward a previous experiment](http://nbviewer.ipython.org/github/flowersteam/explauto/blob/master/notebook/fast_forward_experiment.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.*

• [numpy](http://www.numpy.org)

• [scipy](http://www.scipy.org)

• [scikit-learn](http://scikit-learn.org/)

For more details, please refer to the [installation section](http://flowersteam.github.io/explauto/installation.html) of the documentation.