Lecture location: ITE 127
Lecture time: TuTh 9:30 - 10:45 AM

Class Schedule

Course Description

This course introduces the concepts and algorithms of Reinforcement Learning (RL). It covers Markov decision process (MDP) and its tabular solvers such as policy iteration, value iteration, SARSA, Q-learning, as well as the modern deep neural network based RL solvers, such as DQN, Policy Gradient, Actor-Critic. Function approximators including MLP, CNN, LSTM, Transformer will be presented. Depending on the course progess, selected topics such as AlphaGo, AlphaGo Zero, and Inverse Reinforcement Learning will be discussed. The class emphasizes on the understanding of RL algorithms as well as their practical implementations with Python.

Textbook (optional)

Reinforcement Learning: An Introduction (2nd Edition), Richard S. Sutton and Andrew G. Barto, MIT Press, 2020

Prerequisites

• This should not be your first machine learning class! You should have taken CSE 4820 or 5819, CSE 3500, and STAT 3025Q or 3345Q or 3375Q or MATH 3160.
• Basic knowledge in statistics, linear algebra, and calculus
• Proficiency in Python: All HWs will be in Python

Course Objectives

• The core and advanced methodologies, algorithms, and tools of RL techniques.
• The skills of implementing and applying RL algorithms and models.
• Ideally, developing RL algorithms and models to solve real-world problems.

Grading

• No late submissions accepted! All the assignment and project deadlines are 11:59 pm (EST) of the due dates.
• If you have doubts in your grading, please email TA and CC to the Instructor indicating the reason why you think it should be regraded.
• The regrading request should be submitted within 1 week after you receive your score.

Misc

• Course materials, assignments, projects and QAs are managed by HuskyCT.

Student Responsibilities and Resources

• The Student Code, Academic Integrity, Resources on Avoiding Cheating and Plagiarism
• Copyrighted Materials
• Netiquette and Communication
• Adding or Dropping a Course
• Academic Calendar
• Policy Against Discrimination, Harassment, and Inappropriate Romantic Relationships
• Sexual Assault Reporting