Technical Workshop Series
Foundations of Q-learning (2nd Offering)
Presenter: Xiaofeng (Michael) Liu
Date: Thursday, June 6th, 2024
Time: 12:30 PM
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
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 that achieves learning by interaction with the environment instead of transition probabilities. This workshop will introduce the principle of Q-learning.
Workshop Outline:
• What Q-learning is
• Characteristics of Q-learning models
• What Q-values are
• What temporal differences are
• What the Bellman Equation is
• How the Q-learning process works
• Practice
Prerequisites:
Principle of RL
Biography:
Michael is a PhD Candidate in Computer Science. His main research interests are mainly focused on Congestion Control for V2V communication in VANET (Vehicular Ad hoc Network).