Ph.D. Course: An Introduction to Reinforcement Learning - Academic Year 2022-23

Overview

In this module we will cover the fundamentals of Reinforcement Learning. We will discuss key recent papers in this area and we will outline the open challenges in this field.

Prerequisites

The prerequisites of the module are:

• an in-depth understanding of AI fundamentals;
• a working knowledge of Deep Learning.

Calendar of the Lectures (Provisional)

Friday 22 September 4-6pm

Thursday 28 September 4-6pm

Friday 6 October 4-6pm

Thursday 12 October 4-6pm

Thursday 19 October 4-6pm

Friday 20 October 4-6pm

Friday 27 October 4-6pm

Tuesday 31 October 4-6pm

The module will be delivered through Microsoft Teams. The enrolled students will receive a link before the classes.

Resources

An extensive list of resources will be provided during the module.

Assessment

Students will be invited to present and discuss papers during the module.

Enrolment

In order to enroll you should fill this form. The information will be used only for sending information about this module.

The deadline for enrolment is 15 September 2023 23:59pm.

If you are not a student of the Department of Computer Science and Engineering, please contact the instructor.

Slides

Key Concepts in Reinforcement Learning

Value Function Approximation in Reinforcement Learning

Last updated: 6 October 2023.