Deep Reinforcement Learning - University of The Punjab

Instructor: Nazar Khan
Semester: Fall, 2025
Credit Hours: 3
Level: Graduate
Location: FCIT, Allama Iqbal Campus, Room 10
Class Times: Mon. and Wed., 8:15 AM - 9:45 AM

Google Classroom

Textbook: DRL

Deep Reinforcement Learning
Aske Plaat
Springer Nature, 2022
Preprint

Reference 1: RL

Reinforcement Learning: An Introduction, 2nd Edition
Richard Sutton and Andrew Barto
MIT Press, 2018
PDF

Reference 2: DRLFRA

Deep Reinforcement Learning: Fundamentals, Research and Applications
Springer, 2020

In recent years, Deep Reinforcement Learning (Deep RL) has emerged as one of the most exciting and rapidly advancing fields in artificial intelligence. Combining the sequential decision-making framework of reinforcement learning with the powerful function approximation capabilities of deep neural networks, Deep RL has enabled breakthroughs in areas ranging from game playing—such as AlphaGo and OpenAI Five—to robotics, autonomous driving, operations research, financial systems, and large language models.

Lectures

Lecture	Topic	Readings	Tutorials & Evaluations
1	Introduction	DRL: Ch 1
2	Markov Decision Process (MDP)	DRL: Sec 2.1 - 2.2.2
3	MDP Objective Function	DRL: Sec 2.2.3
4	Model-Based Learning	DRL: Sec 2.2.4.1	GridWorld via Value Iteration .ipynb, .html
5	Model-Free Learning	DRL: Sec 2.2.4.2 - 5

Deep Reinforcement Learning (CS-866)

Department of Computer Science
University of The Punjab

Lectures

Grading