Event  Date  Description  Course Materials  

Lecture  Jan 6  Introduction to Reinforcement Learning 


Lecture  Jan 8  Tabular MDP planning 


A1  Jan 8  Assignment 1 released  
Lecture  Jan 13  Tabular RL policy evaluation  
Lecture  Jan 15  Qlearning 


Jan 20  No Lecture  
Lecture  Jan 22  RL with function approximation 


A1  Jan 22  Assignment 1 due, 11:59pm 
[Assignment 1] > 

A2  Jan 22  Assignment 2 released  
Lecture  Jan 27  RL with function approximation  
Lecture  Jan 29  RL with function approximation  
Lecture  Feb 3  Policy search 


Project  Feb 3  Project proposal due, 11:59pm  
Lecture  Feb 5  Policy search 


Project  Feb 5  Assignment 2 due, 11:59pm  
Exam  Feb 10  Inclass Midterm  Past years have similar but not identical material. Prior midterms include: 2017 exam, 2017 solution, 2018 exam, 2018 solution, 2019 exam and 2019 solution  
A3  Feb 10  Assignment 3 released  
Lecture  Feb 12  Imitation learning / Exploration  
Lecture  Feb 17  No Class: President's Day Holiday  
Lecture  Feb 19  Exploration/Exploitation 


Lecture  Feb 24  Exploration / Exploitation 


A3  Feb 24  Assignment 3 due, 11:59pm  
Lecture  Feb 26  Batch Reinforcement Learning 


Project  Feb 26  Project Milestone due, 11:59pm  
Lecture  Mar 2  Guest Lecture: Craig Boutilier  
Exam  Mar 4  Inclass Quiz  
Lecture  Mar 9  Monte Carlo Tree Search 


Project  Mar 11  Poster presentations 
Class attendance required, requested to stay for 3 hrs. 11:30am2:30pm in the Huang Foyer (area outside of NVIDIA auditorium). 

Project  Mar 18  Project final paper due, 11:59pm 