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ELEC-E8125 - Reinforcement learning D, Lecture, 13.9.2021-8.12.2021

This course space end date is set to 08.12.2021 Search Courses: ELEC-E8125

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Syllabus

Assignments

  • Assignments

    Assignments

    Rules and arrangements

    The course will have six compulsory individual assignments making up 50% of the final grade. The assignments will be introduced in the exercise sessions. Instructions and materials will appear on this page.

    Each assignment will be graded and the assignments constitute towards the course grade.

    Keep in mind that the assignments and quizzes are to be completed individually by each student. While it is perfectly fine to discuss the algorithms, implementations and the concepts taught in the course with your peers, directly sharing answers, data or code will not be accepted. In short—share ideas, not answers.

    Remember to submit all your solutions on time, and double check that your submission contains all the necessary files, as listed at the end of the assignment instruction document. We do not accept any submissions or additional files after the submission system in MyCourses closes. The only exceptions are in well justified cases such as illness (supported by a proper certificate) or military service. If you cannot submit the assignment on time due to university-related reasons, such as attending a conference, please inform the course staff in advance.

    Exercise Sessions

    There are three exercise sessions per week in which you can ask questions about the lectures and exercises. Attendance is optional. The sessions take place online: https://aalto.zoom.us/j/64468034932?pwd=RlpYd2grYkNVQUdJKy8zQWF6VHdKdz09

    H02 Exercises - Monday 12:15-14:00

    H03 Exercises - Tuesday 12:15-14:00

    H01 Exercises - Wednesday 10:15-12:00

    Quizzes

    The quizzes are individual works and should be completed independently. The answers can be found in the lectures and readings. The quizzes will make up 20% of the final grade.

    Deadlines

    The hard-deadlines for each assignments is listed below. There are three weeks from when the assignment is uploaded until the deadline.

    • Exercise 1 - Release: 13.09.2021 - Deadline 04.10.2021

    • Exercise 2 - Release: 20.09.2021 - Deadline 11.10.2021

    • Exercise 3 - Release: 27.09.2021 - Deadline 18.10.2021

    • Exercise 4 - Release: 04.10.2021 - Deadline 25.10.2021

    • Exercise 5 - Release: 11.10.2021 - Deadline 01.11.2021

    • Exercise 6 - Release: 18.10.2021 - Deadline 08.11.2021



    • icon for activity ChoiceExercise sessions on campus Choice
    • Assignments Setting-up and Introduction

    • icon for activity FileSetting Things Up and Submission Instructions File PDF document
    • icon for activity FileExercise intro slides File PDF document
    • icon for activity FileExercise Report Latex Template File Archive (ZIP)
    • icon for activity FolderPyTorch intro Folder
    • Exercises

    • icon for activity AssignmentExercise 1 - Reinforcement Learning introduction Assignment
    • icon for activity FileExercise Week 1 - Intro File Video file (MP4)
    • icon for activity AssignmentExercise 2 - Value iteration Assignment
    • icon for activity AssignmentExercise 3 - Grid-based Q-Learning Assignment
    • icon for activity AssignmentExercise 4 - Q-Learning with Function Approximation Assignment
    • icon for activity FileExercise 4 Intro File Video file (MP4)
    • icon for activity URLMnih, Volodymyr, et al. "Playing Atari with Deep Reinforcement Learning". (2013) URL
    • icon for activity AssignmentExercise 5 - Policy Gradient Assignment
    • icon for activity AssignmentExercise 6 - Actor Critic Assignment
    • Quizzes

    • icon for activity QuizQuiz 1
    • icon for activity QuizQuiz 2
    • icon for activity QuizQuiz 3
    • icon for activity QuizQuiz 4
    • icon for activity QuizQuiz 5
    • icon for activity QuizQuiz 6
    • icon for activity QuizQuiz 7

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  • ELEC-E8125 - Reinforcement learning D, Lecture, 13.9.2021-8.12.2021
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  • CORONAVIRUS INFO
    • Koronavirus - tietoa opiskelijalle
    • Coronavirus - information for students
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    • Corona help for teachers
  • Service Links
    • MyCourses
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    • - Teacher book your online session with a specialist
    • - Digital tools for teaching
    • - Personal data protection instructions for teachers
    • - Instructions for Students
    • - Workspace for thesis supervision
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    • Into portal for students
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    • - Imagoa / Open science and images
    • IT Services
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    • - Search spaces and see opening hours
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    • ASU Aalto Student Union
    • Aalto Marketplace
  • ALLWELL?
    • Study Skills
    • Support for Studying
    • Starting Point of Wellbeing
    • About AllWell? study well-being questionnaire
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