MSc Thesis Proposal of Daniel Obawole:"Comparative Study of Reinforcement Learning Methods in Path Planning "

 

Date: Friday February 26th 2021 

Time:  12:30 pm – 2:00pm 

Zoom URL: https://zoom.us/j/92745094610? 

Passcode: If interested in attending the event, contact the Graduate Secretary at csgradinfo@uwindsor.ca 

 

In order to perform a large variety of tasks and achieve human-level performance in complex real-world environments, an intelligent agent must be able to learn from its dynamically changing environment. Generally speaking, agents have limitations in obtaining an accurate description of the environment from what they perceive because they may not have all the information about the environment. The present research is focused on reinforcement learning algorithms that represent a defined category in the field of machine learning because of their unique approach based on a trial-error basis. Reinforcement learning is used to solve control problems based on received rewards. The core of its learning task is defined by a reward function where an unsuitable choice of action results in more negative rewards. The reinforcement learning framework comprises of the notion of cumulative rewards over time, to enable an agent to select actions that promote long-term results. Q-learning and SARSA are two popular methods along this approach. These two methods are similar except that Q-learning follows an off-policy strategy while SARSA is an on-policy algorithm. The purpose of this thesis is to compare Q-learning and SARSA algorithms for the global path planning of an agent in a grid-world game environment in order to verify the efficiency in different scenarios. 

 

Keywords: Machine learning, Reinforcement learning, Q-learning, SARSA algorithm  

 

Internal Reader: Dr. Dan Wu  

External Reader: Dr. Myron Hlynka   

Advisor: Dr. Jessica Chen