Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN426Deep Reinforcement Learning Laboratory0+2+01412.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To teach student to implement common RL algorithms.
Course Content Introduction to reinforcement learning (RL), Markov decision processes, Planning by Dynamic Programming, Monte Carlo methods, Temporal difference learning, RL with function approximation, Policy Gradient Methods.
Course Methods and Techniques Laboratory, project
Prerequisites and co-requisities ( BBM104 ) and ( BBM102 ) and ( AIN424 )
Course Coordinator None
Name of Lecturers Instructor Bölüm Sorumluları
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition
Course Notes Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Laboratory Work 5 % 60
Final examination 1 % 40
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 2 28
Assignments 5 5 25
General Exam Preparation 1 60 60
Total Work Load   Number of ECTS Credits 3,76666666666667 113

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 To be able to implement common RL algorithms
2 To be able to assess and compare performance of different RL algorithms
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to reinforcement learning
2 Introduction to reinforcement learning
3 Markov decision processes
4 Markov decision processes
5 Planning by Dynamic Programming
6 Planning by Dynamic Programming
7 Monte Carlo methods
8 Monte Carlo methods
9 Temporal difference learning
10 Temporal difference learning
11 RL with function approximation
12 RL with function approximation
13 Policy Gradient Methods
14 Policy Gradient Methods
15 Final exam preparation
16 Final exam (Project)

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 5 5 5 5 5 2 1 2 1 1
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2733658&lang=en