This will be updated regularly throughout the Semester to reflect how to prepare for the next lecture and what is covered during each lecture along with corresponding readings and notes.
Date | Event | Description | Materials and Instructions |
---|---|---|---|
Jan 14 |
Lecture 1 |
Introduction to Reinforcement Learning Basic probability theory review. |
Additional Reading: |
Jan 21 |
Lecture 2 |
Markov Process, Markov Reward Process. Markov Decision Process, Policy Evaluation. |
Additional Reading: |
Jan 24 |
Assignment 1 |
Assignment 1 will be posted online. |
|
Jan 28 |
Lecture 3 |
Policy Improvement, Policy Iteration, Value Iteration |
Additional Reading: |
Feb 4 |
Lecture 4 |
Monte Carlo and Time Difference Methods, Q-Learning |
Additional Reading: |
Feb 11 |
Lecture 5 |
Value Function Approximation, Deep Learning |
Additional Reading: |
Feb 14 |
Assignment 1 |
Assignment 1 due at midnight (i.e., 11:59 PM, 23:59) eastern time. |
|
Feb 15 |
Assignment 2 |
Assignment 2 will be posted online. |
|
Feb 18 |
Lecture 6 |
Deep learning, Deep Q-Learning |
Additional Reading: |
Mar 4 |
Lecture 7 |
Imitation learning. Policy Search. |
Additional Reading: |
Mar 11 |
Lecture 8 |
Policy Search. Multi-armed bandit. Exploration |
Additional Reading: |
Mar 14 |
Assignment 2 |
Assignment 2 due at midnight (i.e., 11:59 PM, 23:59) eastern time. |
|
Mar 18 |
Lecture 9 |
Exploration/Exploitation. Batch Reinforcement Learning |
Additional Reading: |
Mar 25 |
Lecture 10 |
Monte Carlo Tree Search. Students Paper Presentations. |
Additional Reading: |
Apr 1 |
Lecture 11 |
Students Paper Presentations. |
Additional Reading: |
Apr 8 |
Lecture 12 |
Students Paper Presentations. |
Additional Reading: |
Apr 19 |
Final Project |
Final Project report due at midnight (i.e., 11:59 PM, 23:59) eastern time. |