CSE 5820
 Reinforcement Learning

Course Schedule

(Tentative schedule for the course and deviations may be necessary as the course progresses.)

Date Topic Notes Reading (Textbook or Other Materials)
Jan. 20 Introduction RL: Chapter 1, Nature, Basic calculus refresher
Jan. 22 Machine Learning Basics HW1 Q5 Machine learning math essentials
Jan. 29 Sequential Decision Making
RL: Chapter 1
Feb. 3 Markov Decision Processes (MDP)
Project Details
 RL: Chapter 3
Feb. 5
MDP Bellman Equations
 RL: Chapter 3
Feb. 10
Policy Iteration, Value Iteration
 RL: Chapter 4
Feb. 12
Model-free Prediction: MC vs. TD
RL: Chapter 5,6,7
Feb. 17 Model-free Control: SARSA, Q-Learning RL: Chapter 6
Feb. 26 Function Approximation: Linear Classifiers RL: Chapter 9
Mar. 3 Computational Graphs, Backpropagation DL: Chapter 8
Mar. 5 Neural Networks, MLP
Mar. 10 Convolutional Neural Networks AlexNet, ResNet
Mar. 12 Recurrent Nerual Networks, LSTM
Mar. 24 Transformer Attention is all you need, tutorial
Mar. 26 Deep Q Network (DQN)
Mar. 31 Policy Gradient Homework4 details
Apr. 2 Off-policy PG, PPO
Apr. 7 Actor-Critic
Apr. 9 Sparse Reward Faulty reward, HER
Apr. 14 Imitation Learning IRL
Apr. 16 Model-based RL
Mar. 21 Review
Apr. 23 Presentation
Apr. 28 Presentation
Apr. 30 Presentation
May 5 Final Exam
May 8 Project Report Due (by 11:59 pm)

 

 

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