Study Guide

Objective

You understand the core principles behind reinforcement learning
You understand the differences of reinforcement learning regarding classic machine learning and conventional deep learning
You can use conventional reinforcement learning solutions to create an AI that functions in a limited moving space
You can use deep learning methods in order to create situational reinforcement learning solutions
You can share your results and exercises via a version control system.

Content

Basics of reinforcement learning concepts (including exploration and exploitation)
Markov Decision Processes, Monte Carlo Methods, Bellman Equation
Policies: evaluation, improvement, iteration
Conventional Reinforcement learning
Deep Reinforcement Learning

Location and time

Lapland University of Applied Sciences, Rantavitikka Campus, 13.1.2025 - 15.5.2025.

Teaching methods

Lectures, workshops, examples, exercises and self-supervised work.

Exam schedules

The course will be graded based on personal work and exercises.

Content scheduling

Basics of reinforcement learning concepts (including exploration and exploitation)
Common reinforcement learning methods and processes
Policies: evaluation, improvement, iteration
Conventional Reinforcement learning
Deep Reinforcement Learning

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

You can create a simple reinforcement application
You are aware of the basic principles behind reinforcement learning
You understand the difference of reinforcement learning when compared to other conventional machine learning technologies
You can share your results and exercises via a version control system.

Assessment criteria, good (3)

You can create various reinforcement applications, using both conventional methods and deep learning methods
You understand the basic principles behind reinforcement learning on the general level
You understand the difference of reinforcement learning when compared to other conventional machine learning technologies
You can share your results and exercises via a version control system.

Assessment criteria, excellent (5)

You can create various reinforcement applications, using both conventional methods and deep learning methods
You understand the basic principles behind reinforcement learning on the general level
You understand the difference of reinforcement learning when compared to other conventional machine learning technologies
You can optimize your reinforcement learning applications to improve performance
You can share your results and exercises via a version control system.

Assessment methods and criteria

The course will be graded on the scale of 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.

Qualifications

Basics of programming
Basics of Python data analytics modules/libraries
Basics of conventional machine learning methods
Basics of Deep Learning