Introduction

This is the tutorial repository used for the workshop "When Bayesian optimization meets real-time control" held at European Control Conference 2024, Stockholm, Sweden.

Throughout this tutorial, we will use the programming language python. If you are not familiar with python, you may refer to this python tutorial. We will also use jupyter notebook. If you are not familiar with this, please be referred to this jupyter notebook tutorial.

Requirements

gym==0.24.1
pygame==2.5.2
numpy==1.24.4
matplotlib==3.7.1
IPython>=7.23.1
moviepy==1.0.3
decorator>=4.0.2,<5.0
GPy==1.10.0
ax-platform==0.3.3
jupyter
ipykernel>=6.29.4

Installation of requirements

To install the required dependencies, you can use the requirements.txt file. Make sure you have pip installed. We also recommend using python 3.8 or 3.9. The installation process were tested using python 3.9.0 and 3.8.5.

Clone the repository:

git clone https://github.com/your-username/your-repository.git
cd your-repository

Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the dependencies:
```
pip install -r requirements.txt
```

Structure

This tutorial includes the following parts,

Implement BO algorithm using the Gaussian process package GPy:

'demo1.ipynb': Demonstrate the implementation of a Bayesian optimization algorithm.
'exercise1.ipynb': Exercise for you to implement a Bayesian optimization algorithm by yourself.

Use Ax to solve problems.