Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FRT210F valid from Spring 2019

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  • To give the students practical experience of the development of a reinforcement learning application
  • Definition of project
    Implementation of project
    Presentation of project
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • be able to implement a working reinforcement learning system
Competences and Skills
  • For a passing grade the doctoral student must
  • - be able to program in a language suitable for reinforcement learning implementation
Judgement and Approach
  • For a passing grade the doctoral student must
  • - show compeketence in implementation of reinforcement learning
Types of Instruction
  • Project
  • Pure project course
Examination Formats
  • Written report
  • Seminars given by participants
  • Failed, pass
Admission Requirements
  • PhD student at an engineering faculty
Assumed Prior Knowledge
  • Knowledge on reinforcement learning
Selection Criteria
  • None
  • Pure project course
Further Information
  • Project addendum to the course "Study Circle in Reinforcement Learning
Course code
  • FRT210F
Administrative Information
  • 2019-09-12
  • Professor Thomas Johansson

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