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All Projects → befelix → Safeopt

befelix / Safeopt

Licence: mit
Safe Bayesian Optimization

Programming Languages

python
139335 projects - #7 most used programming language

Labels

reinforcement-learningroboticsoptimizationgaussian-processes

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==================================== SafeOpt - Safe Bayesian Optimization

.. image:: https://travis-ci.org/befelix/SafeOpt.svg?branch=master :target: https://travis-ci.org/befelix/SafeOpt :alt: Build Status .. image:: https://readthedocs.org/projects/safeopt/badge/?version=latest :target: http://safeopt.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also provides a more scalable implementation based on [3]_ as well as an implementation for the original algorithm in [4]_. The code can be used to automatically optimize a performance measures subject to a safety constraint by adapting parameters. The prefered way of citing this code is by referring to [1] or [2].

.. image:: http://img.youtube.com/vi/GiqNQdzc5TI/0.jpg :target: http://www.youtube.com/watch?feature=player_embedded&v=GiqNQdzc5TI :alt: Youtube video

.. [1] F. Berkenkamp, A. P. Schoellig, A. Krause, Safe Controller Optimization for Quadrotors with Gaussian Processes <http://arxiv.org/abs/1509.01066>_ in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 491-496.

.. [2] F. Berkenkamp, A. Krause, A. P. Schoellig, Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics <http://arxiv.org/abs/1602.04450>_, ArXiv, 2016, arXiv:1602.04450 [cs.RO].

.. [3] Rikky R.P.R. Duivenvoorden, Felix Berkenkamp, Nicolas Carion, Andreas Krause, Angela P. Schoellig, Constrained Bayesian optimization with Particle Swarms for Safe Adaptive Controller Tuning <http://www.dynsyslab.org/wp-content/papercite-data/pdf/duivenvoorden-ifac17.pdf>_, in Proc. of the IFAC (International Federation of Automatic Control) World Congress, 2017.

.. [4] Y. Sui, A. Gotovos, J. W. Burdick, and A. Krause, Safe exploration for optimization with Gaussian processes <https://las.inf.ethz.ch/files/sui15icml-long.pdf>_ in Proc. of the International Conference on Machine Learning (ICML), 2015, pp. 997–1005.

Warning: Maintenance mode

This package is no longer actively maintained. That bein said, pull requests to add functionality or fix bugs are always welcome.

Installation

The easiest way to install the necessary python libraries is by installing pip (e.g. apt-get install python-pip on Ubuntu) and running

pip install safeopt

Alternatively you can clone the repository and install it using

python setup.py install

Usage

The easiest way to get familiar with the library is to run the interactive example ipython notebooks!

Make sure that the ipywidgets module is installed. All functions and classes are documented on Read The Docs <http://safeopt.readthedocs.org/en/latest/>_.

License

The code is licenced under the MIT license and free to use by anyone without any restrictions.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].