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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRT240F valid from Spring 2021

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General
Aim
  • To teach the participants basic topics in deep reinforcement learning
Contents
  • Q functions, Advanced Policy Gradients, Model Based planning, Model Based RL, Model Based Policy Learning, Control as Inference, Inverse Reinforcement Learning, Distributed RL, Challenges and Open problems
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • understand advanced topics in reinforcement learning and implementation aspects of deep reinforcement learning using neural networks.
Competences and Skills
  • For a passing grade the doctoral student must
  • be able to implement different deep reinforcement learning techniques based on Neural Networks.
Judgement and Approach
  • For a passing grade the doctoral student must
  • understand the advantages and drawbacks of application of reinforcement learning techniques.
    be able to select a good learning environment for effective reinforcement learning.
Types of Instruction
  • Seminars
  • Exercises
  • Self-study literature review
  • Miscellaneous
  • based on Berkely deep RL course "CS285" and a few assignments available on Github
Examination Formats
  • Miscellaneous
  • Active participation in the course
  • Failed, pass
Admission Requirements
  • Admitted to PhD program at an engineering faculty
Assumed Prior Knowledge
  • Basic reinforcement learning knowledge, basic neural network construction and training using tensorflow or pytorch
Selection Criteria
  • None
Literature
  • Sutton, Richard S. & Barto, Andrew G.: Reinforcement Learning, Second Edition. ISBN 9780262039246.
Further Information
Course code
  • FRT240F
Administrative Information
  • 2021-02-18
  • Professor Thomas Johansson

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