Tentative Class Schedule


CSE 6369
Reinforcement Learning
Tentative Lecture and Assignment Schedule
Spring Semester 2020 - TTh 3:30 - 4:50
Class Date Readings Lecture Topics Assignments
1 01/21 1.1 - 1.6 Course Overview and Introduction  
2 01/23   Background - Utility and Decision Theory  
3 01/28 2.1 - 2-10 N-Armed Bandit Problems  
4 01/30 3.1 - 3.8 Background - Probabilistic models and MDPs  
5 02/04 4.1 - 4.2, 4.4 Dynamic Programming Methods - Value Iteration  
6 02/06 4.3 - 4.8 Dynamic Programming Methods - Policy Iteration  
7 02/11 5.1-5.10 Monte Carlo Methods Homework 1 due
8 02/13   Temporal Difference Learning Quiz 1
9 02/18   Actor-Critic Models  
10 02/20   Actor-Critic Models  
11 02/25   On-Policy vs Off-Policy Learning  
12 02/27   Function Approximation in Reinforcement Learning  
13 03/03   Function Approximation in Reinforcement Learning Project 1 due
14 03/05   Exploration vs. Exploitation Tradeoff Quiz 2
  03/10   Spring Break - No Class  
  03/12   Spring Break - No Class  
15 03/17   Model-Based Learning  
16 03/19   Learning Models for Reinforcement Learning  
17 03/24   Learning Models for Reinforcement Learning Homework 2 due
18 03/26   Efficient Model-Based Learning Quiz 3
19 03/31   Efficient Model-Based Learning  
20 04/02   Learning in Partially Observable Systems  
21 04/07   Learning in Partially Observable Systems Project 2 due
22 04/09   Learning in Partially Observable Systems Quiz 4
23 04/14   Hierarchical Reinforcement Learning  
24 04/16   Hierarchical Reinforcement Learning  
25 04/21   Hierarchical Reinforcement Learning Homework 3 due
26 04/23   Inverse Reinforcement Learning Quiz 5
27 04/28   Reinforcement Learning in Multiagent Domains  
28 04/30   Deep Reinforcement Learning  
29 05/05   Deep Reinforcement Learning Project 3 due
30 05/07   Deep Reinforcement Learning & Current Challenges Quiz 6
31 05/14   Final Project Presentations (2:00pm-4:30pm)