Department of Information Technology

Reinforcement learning PhD course (5+3 hp)

Spring 2020, Period 4

Description

Reinforcement learning is a family of modern machine learning techniques which has
obtained unprecedented successes in artificial intelligence benchmarks, see for
instance Google’s AlphaGo’s successes against humans. Using reinforcement
learning techniques, computers can autonomously learn to make decisions using
feed-back from real and/or simulated environment/data. This course will give a PhD
level introduction to these techniques.

In particular, the general course objective is the following:
- Evaluate the applicability and limitations of reinforcement learning (RL) approaches to a given problem. Choose and implement basic forms of a suitable RL method.

Pre-course assignment

The pre-course assignment will be available in February, and it is recommended to solve this assignment as soon as possible. The assignments in the course will require Python programming, and the purpose of the pre-course assignment is to ensure that you have started using Python. If you are new to python the following introductory crash course is also very well suited for the content of this course.

Tentative schedule

The dates may change.

Week Date Lecture
15 April 6 Deadline: Homework 0
16 April 14 Introduction
16 April 15 Markov Decision Processes, Dynamic Programming
16 April 17 Reinforcement learning algorithms I
17 April 22 Reinforcement learning algorithms II
17 April 24 Planning and learning
17 Deadline: Homework 1
19 May 5 Approximation methods
19 May 8 Learning with approximations
19 Deadline: Homework 2
20 May 13 Model-free vs model-based
20 May 15 Additional methods
21 Deadline: Homework 3

Content

Markov Decision Processes, Dynamic Programming (Policy Evaluation, Policy
Iteration, Value Iteration), Model-free RL (Monte-Carlo Learning, Temporal-Difference
Methods, On-Policy and Off Policy Methods), Model-based RL, Deep Learning,
Approximation Methods for RL, Policy Gradient Methods.

Prerequisites

Programming experience, basic courses in linear algebra, probability and optimization.

Examination

3 hand-in assignments and one pre-course assignment (5 credits)

It is also possible to do a project in RL that is relevant to your research for 3 extra credtis.

Lecturers

Ayca Özcelikkale, André Teixeira, Per Mattsson

Contact Person

Per Mattsson, email: per.mattsson_at_it.uu.se

More information will be added to this page soon

Updated  2020-01-21 09:23:55 by Per Mattsson.