ECE 517: Reinforcement Learning in Artificial Intelligence – Fall 2015

* Schedules are subject to changes, please check back frequently
 

Date

Topic/s Covered

Lecture Notes & Handouts

Reading Assignments (before class)

Homework & Projects

Thr - Aug 20

Class overview, Introduction

Lecture #1

 

Word Clouds on Interests and Expectations

* A good reference book on Probability theory)

* Matlab references [1][2]

 

 

Tue - Aug 25

Evaluative Feedback

 

Lecture #2

 

 

 

 

Thr – Aug 27

Review of discrete-time probability fundamentals

 

Lecture #3

 

 

 

Homework #1

Due: September 8

Tue – Sept 1

Discrete Time Markov Chains

 

 

 

Thr – Sep 3

The Reinforcement Learning Problem

 

 

 

 

Tue – Sep 8

The Reinforcement Learning Problem (cont’)

 

 

 

Thr – Sep 10

Markov Decision Processes (MDPs) and Optimality Criterion in MDPs, SW/HW Considerations

 

 

 

Tue – Sep 15

 

Finite Horizon MDP

 

 

Thr – Sep 17


Dynamic Programming, Policy Iteration, Value Iteration

 

(Aaron Mishtal)

 

 

Tue – Sep 22

 

Class Cancelled

 

 

 

Thr – Sep 24

Monte Carlo Methods

 

 

 

Tue – Sept 29

Monte Carlo Methods (cont’)

 

 

 

Thr – Oct 1

Monte Carlo Methods (cont’)

 

 

 

 

Tue – Oct 6

 

In-Class Midterm – MK 405

 

 

 


Thr – Oct 8

 

Temporal Difference Learning

 

 

 

 

Tue – Oct 13

 

Fall break – No Class

 

 

 

Thr – Oct 15

Actor-Critic Model, Eligibility Traces

(Aaron Mishtal)

 

 

 

Tue – Oct 20

Generalization & Function Approximation

 

 

 

Thr – Oct 22

Generalization & Function Approximation (cont’)

 

 

 

Tue – Oct 27

Neural Networks – Introduction, Feedforward Neural Networks

 

 

 

Thr – Oct 29

Neural Networks (cont’)

 

 

 

Tue – Nov 3

 

Planning in RL

 

 

 

Thr – Nov 5

Partially Observable MDPs (POMDPs)

 

 

 

Tue – Nov 10

Recurrent Neural Networks

 

 

 

Thr – Nov 12

Inverse Reinforcement Learning, Imitation Learning in RL

 

 

 

Tue – Nov 17

Deep Reinforcement Learning

 

 

 

Thr – Nov 19

Student Presentations

 

 

 

Tue – Nov 24

Student Presentations

 

 

 

Thr – Nov 26

Thanksgiving – No Class

 

 

 

Tue – Dec 1

Student Presentations

 

 

 

Friday – Dec 4

Final project reports are due

 

 

 

Matlab codes that cover most topics addressed in the course textbook can be found at: http://www.waxworksmath.com/Authors/N_Z/Sutton/sutton.html

Last Update: August 27, 2015