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6.S890 | Fall 2024 | Graduate

# Topics in Multiagent Learning

#### Lecture Notes

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2 MB
Lecture 01: Topics in Multiagent Learning
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356 kB
Lecture 02: Setting and Equilibria: The Nash Equilibrium
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744 kB
Lecture 03: Setting and Equilibria: The Correlated Equilibrium
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488 kB
Lecture 04: Learning in Games: Foundations
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585 kB
Lecture 05: Learning in Games: Algorithms (Part I)
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1 MB
Lecture 06: Learning in Games: Algorithms (Part II)
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442 kB
Lecture 07: Learning in Games: Bandit Feedback
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444 kB
Lecture 08: Learning in Games: Φ-Regret Minimization
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280 kB
Lecture 09: Foundations of Extensive-Form Games
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2 MB
Lecture 09: Foundations of Extensive-Form Games (Slides)
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#### Problem Set Solutions

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498 kB
Problem Set 1 - Reference Solution
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326 kB
Problem Set 2 - Reference Solution
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49 kB
Problem Set 2 - Reference Solution Additional File
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441 kB
Problem Set 3 - Reference Solution

#### Problem Sets

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461 kB
Problem Set 1
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498 kB
Problem Set 1 - Reference Solution
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32 kB
Problem Set 1 Additional Files
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618 kB
Problem Set 2
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326 kB
Problem Set 2 - Reference Solution
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49 kB
Problem Set 2 - Reference Solution Additional File
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45 kB
Problem Set 2 Additional Files
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306 kB
Problem Set 3
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441 kB
Problem Set 3 - Reference Solution
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22 kB
Problem Set 3 Additional File

#### Projects

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291 kB
MIT 6.S890 Additional Projects Ideas
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206 kB
MIT 6.S890 Phantom Tic-Tac-Toe Challenge
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283 kB
MIT 6.S890 Team Leduc Poker Challenge

## Course Info

##### Instructor

- Prof. Gabriele Farina

##### Departments

- Electrical Engineering and Computer Science

##### As Taught In

Fall 2024

##### Level

Graduate

##### Topics

- Engineering
  - Computer Science
    - Artificial Intelligence

##### Learning Resource Types

notes Lecture Notes
assignment_turned_in Problem Set Solutions
assignment Problem Sets
group_work Projects
auto_stories Readings

Browse Resources

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