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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.

Homepage for RL4AA Collaboration: https://rl4aa.github.io/

Workshop organizing committee

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

Slides

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.

Install ffmpeg

  • OS X: brew install ffmpeg
  • Ubuntu: sudo apt-get install ffmpeg
  • Ubuntu 14.04: sudo apt-get install libav-tools
  • With pip: pip install imageio-ffmpeg

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 only

Instructions to install conda here

conda env create -f environment.yml
conda activate rl4aa
jupyter notebook
  • Open the tutorial notebook tutorial.ipynb in the jupyter server in browser
  • When you are done type conda deactivate to deactivate the virtual environment

conda + pip

cd path_to_your_folder/RL4AA23
conda create -n rl4aa python=3.10
conda activate rl4aa
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 to deactivate the virtual environment