EE5241: Optimal Control and Reinforcement Learning

3 CreditsGoal 10 - People/EnvironmentGoal 6 - Hum: Arts/Lit/PhilOnline Available

(Prereq-CSE grad student or instructor consent) A wide variety of control problems such as "walk from home to school via the shortest path" or "maintain a constant temperature" can be modeled using optimization. This course will survey a variety of methods for modeling and solving optimal control problems. In particular, we will cover numerical optimal control, model predictive control, system identification, dynamic programming, and reinforcement learning. Examples from robotics and aerospace systems will be given.

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A- Average (3.631)Most Common: A- (33%)

This total also includes data from semesters with unknown instructors.

55 students
SWFDCBA
  • 4.10

    /5

    Recommend
  • 4.30

    /5

    Effort
  • 4.20

    /5

    Understanding
  • 4.50

    /5

    Interesting
  • 4.40

    /5

    Activities


  • Samyok Nepal

    Website/Infrastructure Lead

  • Kanishk Kacholia

    Backend/Data Lead

  • Joey McIndoo

    Feature Engineering

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