Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRT205F valid from Spring 2019

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  • To become familiar with reinforcement learning.
  • - Markov Decision Processes
    - Dynamic programming
    - Iterative policy evaluation
    - Iterative policy iteration
    - Value iteration
    - Model-free planning
    - Monte-Carlo methods
    - Model-free control
    - Value Function Approximation
    - Policy Gradient Methods
    - Exploration and Exploitation
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • - understand the basic concepts and methods of reinforcement learning
Competences and Skills
  • For a passing grade the doctoral student must
  • - be able to understand the implementation of reinforcement learning using Python and Tensorflow
Judgement and Approach
  • For a passing grade the doctoral student must
  • - understand for which problems reinforcement learning is suitable
Types of Instruction
  • Seminars
  • Exercises
  • Self-study literature review
  • Miscellaneous
  • Based on the Reinforcement Learning Course given by David Silver at UCL and the associated video presentations.
Examination Formats
  • Miscellaneous
  • Sufficient active participation in the course meetings
  • Failed, pass
Admission Requirements
  • Admitted to PhD program at an engineering faculty
Assumed Prior Knowledge
  • None
Selection Criteria
  • None
  • The course is based on video lectures by David Silver.
Further Information
Course code
  • FRT205F
Administrative Information
  • 2019-09-12
  • Professor Thomas Johansson

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