Deep Learning for Games and Simulations (Autumn 2023)

Abstract

The goal of the course is to teach the understanding, design, implementation, and use of modern artificial intelligence (AI) techniques for generating efficient, intelligent behaviors in games and other simulation environments. A particular focus is given to techniques applied to agent-based simulations.

Description

Artificial intelligence has many applications, and many techniques need to be applied in simulated environments, whether during the training (as in robotics or systems simulations) or deployment (such as in computer games). The topics covered in this course apply to games, simulation environments, robotics, and many other areas.

Students will learn a broad understanding of AI algorithms' theoretical, practical, and implementation sides.

The course will partly cover the following topics (AI techniques and problems): 

• neural networks,
• deep reinforcement learning, 
• evolutionary algorithms, 
• neuroevolution,
• Monte Carlo Tree Search (MCTS), 
• hybrid approaches (MCTS+Neural networks), 
• adversarial search,
• multi-agent learning,
• as well as the overview of the latest applications of these techniques in the industry.

Formal prerequisites

Students must have completed a course on programming such as "Introductory Programming" or "Object-Oriented Programming." Furthermore, the student should have familiarity with basic reinforcement learning and graph search algorithms. We will mainly use the Python programming language during the course, so familiarity with the language is an advantage.

Intended learning outcomes

After the course, the student should be able to:

• Theorize about and describe the AI algorithms covered in the class
• Identify tasks that can be tackled through advanced AI techniques and select the appropriate technique for the problem under investigation
• Compare the performance of different AI techniques and reflect on their suitability for different domains
• Design and implement efficient and robust advanced AI algorithms
• Work efficiently in groups

Learning activities

6 weeks of intensive lectures + mandatory individual assignment (see below). 

8 weeks of group project work with supervision (There are some lectures during this period).

Students are responsible for attending lectures (some of which will likely be by outside guest speakers) and working on their projects independently (individual mandatory assignment) or in groups.

Besides the hours planned for lectures, there are tutorials, exercises, and supervision sessions that complement the theory covered during the lectures and are necessary for meeting the course's learning objectives. Students will practice presenting their work during the course to prepare for the oral exam. Lectures provide theoretical foundations and walk-through examples of relevant AI algorithms, while exercises focus on students discussing and implementing the central algorithms themselves.

Mandatory activities

During this course, students will have to hand in an individual mandatory assignment (6 pages written report + source code) that needs to be completed/approved before being eligible for the examination.

The mandatory assignment deadline will be on learnIT. 

The student will receive the grade NA (not approved) at the ordinary exam; if the mandatory activities are not approved, the student will use an exam attempt.

The students will receive formative feedback from the teacher/TA/peers.

The pedagogical function of the mandatory project is to provide the students with an activity where they can practice the given ILO’s.

If the students fail to hand in/fail to pass the mandatory activity they will be given the opportunity to resubmit approximately 2-3 weeks later.  

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

We will publish the course literature on the course page in LearnIT.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 20%
• Lectures: 20%
• Exercises: 10%
• Assignments: 20%
• Project work, supervision included: 20%
• Exam with preparation: 10%

Ordinary exam

Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.

reexam

Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.

Time and date

Ordinary Exam - submission Tue, 2 Jan 2024, 08:00 - 14:00
Ordinary Exam Wed, 17 Jan 2024, 09:00 - 21:00
Reexam - submission Wed, 28 Feb 2024, 08:00 - 14:00
Reexam Mon, 11 Mar 2024, 09:00 - 10:00