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Python Library for Autonomous Exploration

Project description

# 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 provide 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!

## Documentation ##

### Scientific grounding ###

Explauto’s scientific roots trace back from Intelligent Adaptive Curiosity algorithmic architecture [[Oudeyer 07]](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 ###

### API ###

The Explauto documentation on a html format can be found [here](http://flowersteam.github.io/explauto/).

## Installation ##

Explauto is available via pip. It can thus be installed with the classical:

pip install explauto

or:

easy_install explauto

The core of explauto depends of the following packages:

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

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