gpflowpilco's Introduction

GPflowPILCO

A modern implementation of PILCO based on GPflow. This package focuses on model-based reinforcement learning with Gaussian processes. Alongside GPflowSampling, this code is intended as a companion for the paper Pathwise Conditioning of Gaussian Processes.

Package content

Module	Description
`components`	Miscellania such as objective functions and state encoders
`dynamics`	Generic classes for working with dynamical systems
`envs`	Various enviroments based on Gym
`loops`	High-level algorithm suites, such as variants of PILCO
`models`	Gaussian processes and associated methods
`moment_matching`	Compute the moments of, e.g., nonlinear functions of Gaussian rvs
`utils`	Assorted utility methods

Installation

git clone [email protected]:j-wilson/GPflowPILCO.git
cd GPflowPILCO
pip install .

Note that this package relies on GPflowSampling, which should be installed first.

Examples

Code for experiments discussed in Section 7.4 of Pathwise Conditioning of Gaussian Processes is provided in examples/cartpole_swingup.

Citing Us

If our work helps you in a way that you feel warrants reference, please cite the following paper:

@article{wilson2021pathwise,
    title={Pathwise Conditioning of Gaussian Processes},
    author={James T. Wilson
            and Viacheslav Borovitskiy
            and Alexander Terenin
            and Peter Mostowsky
            and Marc Peter Deisenroth},
    booktitle={Journal of Machine Learning Research},
    year={2021},
    url={https://arxiv.org/abs/2002.09309}
}

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