CS 4789

CS 4789

Course information provided by the Courses of Study 2020-2021.

Reinforcement Learning is one of the most popular paradigms for modelling interactive learning and sequential decision making. This course introduces the basics of Reinforcement Learning. The course will cover basics of Markov Decision Process, Planning and Learning in Markov Decision Processes. We will discuss potential applications of Reinforcement Learning. We will study and implement classic Reinforcement Learning algorithms.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: CS 4780.

Outcomes
  • Identify the differences between Reinforcement Learning and traditional Supervised Learning and grasp the key definitions of Markov Decision Processes.
  • Analyze the performance of the class planning algorithms and learning algorithms for Markov Decision Process.
  • Implement classic algorithms and demonstrate their performance on benchmarks.

View Enrollment Information

Enrollment Information
Syllabi: none
  •   Regular Academic Session.  Combined with: CS 5789

  • 3 Credits Graded

  • 18521CS 4789  LEC 001

    • TROnline Meeting
    • Feb 8 - May 14, 2021
    • Sun, W

  • Instruction Mode: Online
    Students who are in Ithaca will be required to take the final exam in-person. Enrollment limited to CS students only. All others should add themselves to the waitlist during add/drop. Please see http://www.cs.cornell.edu/courseinfo/enrollment for more information.