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)