Lectures will be places of discussion where the current topic is summarized by the lecturer and discussed among all present. The students are expected to prepare by reading given material in advance prior to each lecture.
Course lectures will be given during first and second periods on Tuesdays 14:15-16:00. All lectures will be given over Zoom at link https://aalto.zoom.us/j/63645678644. Please download and install Zoom before the first lecture to attend the course. Lecture recordings will likely be available afterwards, but this cannot be guaranteed due to potential technical issues. The lectures are interactive in nature so that participation is encouraged.
Course lectures will be given by Ville Kyrki (first part) and Joni Pajarinen (second part).
Schedule and Readings
For each lecture starting from the third one, there will be reading materials that the students should study before attending the lecture.
Course arrangements, Overview, Tue 14.9., no readings
Markov decision processes, Tue 21.9., Sutton & Barto, chapters 2-2.3, 2.5-2.6, 3-3.8
RL in discrete domains (value-based RL), Tue 28.9., Sutton&Barto Ch. 5-5.4, 5.6, 6-6.5
Function approximation, Tue 5.10., Sutton&Barto Ch. 9-9.3, 10-10.1
Policy gradient, Tue 12.10., Sutton&Barto, Ch. 13-13.3
Actor-critic, Tue 19.10., Sutton & Barto, Ch. 13.5, 13.7
Towards model-based reinforcement learning: optimal control, Tue 26.10. Platt: Introduction to Linear Quadratic Regulation
Model-based reinforcement learning, Tue 2.11., Sutton & Barto, Ch. 8-8.2Guest lectures Tue 9.11.: Safety and constraints (Gökhan Alcal), Entropy Regularization in Reinforcement Learning (Riad Akrour)