Topic outline

  • Overview

    The course provides an overview of mathematical models and algorithms behind optimal decision making in time-series systems. The course focus is on optimal decision making and control, reinforcement learning, and decision making under uncertainty.

    Practical matters

    Lecturer: Joni Pajarinen.

    Teaching assistants (TAs): Yi Zhao, Aleksi Ikkala, Wenshuai Zhao, Nikita Kostin, Ali Khoshvishkaie, Jifei Deng, Mohammadreza Nakhaei.

    • The reinforcement learning lecture will be organized in person this year.
      • Location: Maarintie 8, AS1
      • Time: Tuesdays 14:15-16:00 (Period I, II)
      • Although in person participation is encouraged for the full lecture experience lectures will be also recorded and can be watched afterwards
    • Grading Scale: 0-5
      • 7 individual assignments (60%)
      • 1 project work, in groups (max. 2 students) (20%)
      • Quizzes (due before lecture) (20 %)
    • Exercise sessions will be given twice a week. Attendance is optional.
      • (Remotely) Mondays 12.15–14.00, Zoom Link (links will be given during sessions)
      • (In person) Wednesdays 10.15–12.00, Maarintie 8, AS3 Saab Space
    • Please join the slack channel to receive the latest updates and ask questions about the exercises.  Please use your Aalto account for registering to Slack. Notice that, we will use the slack channel as the main place to answer questions about the exercises.
    • Each Student has 3 days in total for late submissions.


    Week Lecture Lecture Date Reading Events Deadline 
    W36 L1 Course Overview Tue, 6.9 no readings Ex1 (6.9) -
    W37 L2 Markov decision processes Tue, 13.9 Sutton & Barto, chapters 2-2.3, 2.5-2.6, 3-3.8 Ex2(13.9) -
    W38 L3 RL in discrete domains Tue, 20.9 Sutton & Barto Ch. 5-5.4, 5.6, 6-6.5 Ex3(20.9) Ex1 (19.9)
    W39 L4 Function approximation Tue, 27.9 Sutton & Barto Ch. 9-9.3, 10-10.1 Ex4(27.9) Ex2(26.9)
    W40 L5 Policy gradient Tue, 4.10 Sutton & Barto, Ch. 13-13.3 Ex5(4.10) Ex3(3.10)
    W41 L6 Actor-critic Tue 11.10 Sutton & Barto, Ch. 13.5, 13.7 Ex6(11.10) Ex4(10.10)
    W42 No Lecture Tue, 18.10
    W43 L7 Model-based RL Tue, 25.10 Sutton & Barto, Ch. 8 - 8.2

    W44 L8 Interleaved learning and planning Tue, 1.11 Sutton & Barto, Ch. 8 - 8.2  Proj (1.11)
    W45 L9 Exploration and exploitation Tue, 8.11

    1) Sutton & Barto, Ch. 2.7, 8.9 - 8.11 and 2) Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A tutorial on Thompson sampling. Foundations and Trends in Machine Learning, 11(1), 1-96. Section 2, 3, 4

    Ex7(8.11) Ex6(7.11)
    W46 L10 Guest lecture (Aidan Scannell). Model-based reinforcement learning under uncertainty: the importance of knowing what you don't know
    Tue, 15.11
    W47 L11 Partially observable MDPs Tue, 22.11

    1) Anthony Cassandra, POMDP tutorial,, steps from "Brief Introduction to MDPs" until "Background on POMDPs" and 2) Partially Observable Markov Decision Processes in Robotics: A Survey. Sections II.A, III.B, III.C

    W48 No Lecture Tue, 29.11 Project (12.12)

    Who to contact

    Usually, if you need help with the exercises or project work, you can put your questions in the corresponding slack channel or attend the exercise session. But if you need to contact TAs in person, here is the list:

    Ex/Proj    TAs
    Ex1Aleksi, Yi
    Ex2Jifei, Wenshuai
    Ex3Ali, Nikita
    Ex4Jifei, Yi
    Ex5Ali, Aleksi
    Ex6Mohammadreza, Wenshuai
    Ex7Mohammadreza, Yi

    If you have other questions (such as illness or military service, etc), you can directly contact Prof. Joni Pajarinen.