Demonstration code for the "Building a complete RL system" lecture

Introduction

This code demonstrates our implementation of SARSA for the Taxi-v3 environment and serves as additional information to go alongside the "Building a complete RL system" lecture. The lecture is delivered as part of the Reinforcement Learning (2024) course at the University of Edinburgh.

Dependencies

We recommend using python 3.7 or above. To run the python code, you will need Gymnasium, Matplotlib and NumPy which can be installed using

pip install gymnasium gymnasium[toy-text] matplotlib numpy

Jupyter Notebook

We recommend going through the jupyter notebook file of this demonstration in your own time! This includes further information and explanations to understand this demonstration and learn more about good practises in RL evaluation. You can directly view the notebook here or run it yourself with jupyter. To install the required software and run the notebook provided with the code, follow the instructions on the Jupyter mainpage.

Code

All code is written in Python3 and provided as separate scripts and all-together in one Jupyter notebook available here with further explanations.

Contact

This lecture was delivered by the TA team consisting of

• Mhairi Dunion
• Trevor McInroe

For any questions, please post on Piazza or see us at your demonstration sessions.