Required Materials

• RLAI: Reinforcement Learning: An Introduction http://incompleteideas.net/book/the-book.html
• MARL: Multi-Agent Reinforcement Learning https://www.marl-book.com/

Recommended Materials

• MAS: Multiagent Systems https://www.masfoundations.org/
• 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 RL	RLAI: 1 MARL: 1 Syllabus Resources Meet Prof. Simpkins
2	Multi-Armed Bandits	RLAI: 2
3	Markov Decision Processes	RLAI: 3
4	Dynamic Programming	RLAI: 4
5	Monte Carlo Control	RLAI: 5
6	Temporal-Difference Learning	RLAI: 6
7	n-step Bootstrapping	RLAI: 7
8	Tabular Planning and Learning	RLAI: 8
Function Approximation and Deep Reinforcement Learning
9	On-policy Prediction	RLAI: 9
10	On-policy Control	RLAI: 10
11	Off Policy Methods	RLAI: 11
12	Eligibility Traces	RLAI: 12
13	Policy Gradient Methods	RLAI: 13
14	Deep Learning	MARL: 7
15	Deep Reinforcement Learning	MARL: 8
Multi-Agent Reinforcement Learning
16	Games	MARL: 3
17	Game Solutions	MARL: 4
18	MARL in Games	MARL: 5
19	MARL Algorithms	MARL: 6
20	Deep MARL	MARL: 91-9.4
21	Deep MARL Value Decomposition	MARL: 9.5
22	Deep MARL Agent Modeling	MARL: 9.6-9.7
23	Deep MARL Self-Play	MARL: 9.8-9.9
24	Practical Deep MARL	MARL: 10
25	MARL Environments	MARL: 11