KA240301 Reinforcement Learning

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA240301 Subject Classification Specialized Education

Subject Name 強化学習

Subject Name
（Katakana）キョウカガクシュウ

Subject Name in
English Reinforcement Learning

Instructor OGURA MASAKI

Instructor
(Katakana) オグラ　マサキ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Mon1-4：ECON B257

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (on-demand)

Lecture, exercises, blackboard, student presentations

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic.

Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers.
（Abilities and Skills）
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.

Class Objectives
/Class Outline Able to explain the concept of reinforcement learning.
Able to implement reinforcement learning algorithms.

Class Schedule lesson1 Introduction to Artificial Intelligence and Deep Learning
lesson2 Development Environment
lesson3 Fundamentals of Python
lesson4 Simple Deep Learning
lesson5 Theory of Deep Learning
lesson6 Various Machine Learning Methods
lesson7 Convolutional Neural Networks
lesson8 Recurrent Neural Networks
lesson9 Variational Autoencoders
lesson10 Generative Adversarial Networks
lesson11 Reinforcement Learning 1
lesson12 Reinforcement Learning 2
lesson13 Reinforcement Learning 3
lesson14 Reinforcement Learning 4
lesson15 Transfer Learning

Text/Reference
Books,etc. Google Colaboratoryで学ぶ！あたらしい人工知能技術の教科書第2版機械学習・深層学習・強化学習で学ぶAIの基礎技術
，翔泳社

PC or AV used in
Class,etc. Text

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Reviewing the textbook in advance will facilitate your understanding of the material. Additionally, applying the methods introduced in the lectures to other problems will deepen your comprehension of each technique.

Requirements

Grading Method Evaluation will be based on the total score from submitted assignments and in-class reports.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message It is important to understand the fundamental concepts of reinforcement learning. Additionally, maintaining continuous learning and a proactive attitude toward exploring new developments are essential, enabling one to adapt flexibly to emerging reinforcement learning algorithms and methodologies.

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA240301	Subject Classification	Specialized Education
Subject Name	強化学習
Subject Name （Katakana）	キョウカガクシュウ
Subject Name in English	Reinforcement Learning
Instructor	OGURA MASAKI
Instructor (Katakana)	オグラ　マサキ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Mon1-4：ECON B257
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (on-demand)
Lecture, exercises, blackboard, student presentations
Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic. Data Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Knowledge and Understanding）・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. （Abilities and Skills）・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
Class Objectives /Class Outline	Able to explain the concept of reinforcement learning. Able to implement reinforcement learning algorithms.
Class Schedule	lesson1 Introduction to Artificial Intelligence and Deep Learning lesson2 Development Environment lesson3 Fundamentals of Python lesson4 Simple Deep Learning lesson5 Theory of Deep Learning lesson6 Various Machine Learning Methods lesson7 Convolutional Neural Networks lesson8 Recurrent Neural Networks lesson9 Variational Autoencoders lesson10 Generative Adversarial Networks lesson11 Reinforcement Learning 1 lesson12 Reinforcement Learning 2 lesson13 Reinforcement Learning 3 lesson14 Reinforcement Learning 4 lesson15 Transfer Learning
Text/Reference Books,etc.	Google Colaboratoryで学ぶ！あたらしい人工知能技術の教科書第2版機械学習・深層学習・強化学習で学ぶAIの基礎技術，翔泳社
PC or AV used in Class,etc.	Text
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Reviewing the textbook in advance will facilitate your understanding of the material. Additionally, applying the methods introduced in the lectures to other problems will deepen your comprehension of each technique.
Requirements
Grading Method	Evaluation will be based on the total score from submitted assignments and in-class reports.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	It is important to understand the fundamental concepts of reinforcement learning. Additionally, maintaining continuous learning and a proactive attitude toward exploring new developments are essential, enabling one to adapt flexibly to emerging reinforcement learning algorithms and methodologies.
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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