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Code for the of 1st collaboration workshop on Reinforcement Learning for Autonomous Accelerators (RL4AA'23)

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1st collaboration workshop on Reinforcement Learning for Autonomous Accelerators (RL4AA'23)

This repository contains the material for the second day of the RL4AA'23 event.

Workshop organizing committee

  • Andrea Santamaria Garcia (KIT)
  • Simon Hirländer (University of Salzburg)
  • Jan Kaiser (DESY)
  • Chenran Xu (KIT)

Slides

  • Welcome and basics of RL, Andrea Santamaria Garcia
  • Advanced concepts in RL, Simon Hirländer
  • RL in particle accelerator control: are we there yet?, Simon Hirländer

Python tutorial: reinforcement learning implementation example

Getting started

  • First, download the material to your local disk by cloning the repository: git clone https://github.com/ansantam/RL4AA23
  • If you don't have git installed, you can click on the green button that says "Code", and choose to download it as a .zip file.

Setup the environment locally

  • Open terminal app
  • (Suggested) Create a virtual envrionment using conda or venv.

venv

python3 -m venv rl4aa
source rl4aa/bin/activate
pip3 install -r requirements.txt
jupyter notebook
  • Open the tutorial notebook tutorial.ipynb in the jupyter server in browser
  • When you are done type deactivate

conda

Instructions to install conda here

conda create -n rl4aa python=3.10
conda activate rl4aa
cd path_to_your_folder/RL4AA23
pip3 install -r requirements.txt
jupyter notebook
  • Open the tutorial notebook tutorial.ipynb in the jupyter server in browser
  • When you are done type conda deactivate

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Code for the of 1st collaboration workshop on Reinforcement Learning for Autonomous Accelerators (RL4AA'23)

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