Technical Workshop Series
A Python Example of Q-learning (2nd Offering)
Presenter: Xiaofeng Liu (Michael)
Date: Thursday, June 13th, 2024
Time: 12:30 PM
Location: 4th Floor (Lecture Space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
This is a hands-on workshop; it is recommended that you bring your laptop.
Abstract:
Reinforcement learning (RL) is training machine learning models to make a sequence of decisions. Q-learning is a type of RL with a model-free environment which achieves learning by interaction with the environment instead of transition probabilities. This workshop will give a Python example of how to use Q-learning to solve a shortest-path problem.
Workshop Outline:
-To review the principle of RL and Q-learning
-To define the environment
-To implement Q-learning with Jupyter notebook
Prerequisites:
Principle of RL, Foundation of Q-learning, Python, Jupyter notebook
Biography:
Xiaofeng is a PhD Candidate in Computer Science. His research interests mainly focus on Congestion Control for V2V communication in VANET (Vehicular Ad hoc Network).