Required Materials

• RLAI: Reinforcement Learning: An Introduction http://incompleteideas.net/book/the-book.html

Recommended Materials

• AIMA: Artificial Intelligence: A Modern Approach, 4ed, by Stuart Russell and Peter Norvig, https://aima.cs.berkeley.edu/. US or Global Edition
• PMLAI: Probabilistic Machine Learning: An Introduction https://probml.github.io/pml-book/book1.html

Schedule

#	Topics	Reading/Resources	Exercises
Tabular Solution Methods
1	Introduction to the Course	Syllabus Resources Meet Prof. Simpkins
2	Introduction to RL	AIMA: 1
3	Multi-Armed Bandits	RLAI: 2
4	Markov Decision Processes	RLAI: 3
5	Dynamic Programming	RLAI: 4
6	Monte Carlo Control	RLAI: 6
7	Temporal-Difference Learning	RLAI: 6
8	n-step Bootstrapping	RLAI: 7
9	Tabular Planning and Learning	RLAI: 8
Function Approximation and Deep Reinforcement Learning
10	On-policy Prediction	RLAI: 9
11	On-policy Control	RLAI: 10
12	Off Policy Methods	RLAI: 11
13	Eligibility Traces	RLAI: 12
14	Policy Gradient Methods	RLAI: 13
15	Deep Learning	MARL: 7
16	Deep Reinforcement Learning	MARL: 8
Multi-Agent Reinforcement Learning
17	Games	MARL: 3
18	Game Solutions	MARL: 4
19	MARL in Games	MARL: 5
20	MARL Algorithms	MARL: 6
21	Deep MARL	MARL: 9
22	Deep MARL	MARL: 9
23	Deep MARL	MARL: 9
24	Deep MARL	MARL: 9
25	Practical Deep MARL	MARL: 10
26	MARL Environments	MARL: 11