Multi-Agent Reinforcement Learning - Part I

Explore the foundations of Multi-Agent Reinforcement Learning in this comprehensive lecture by Princeton University's Chi Jin. Delve into classical game theory concepts, reinforcement learning principles, and their intersection in multi-agent systems. Examine various formulations, objectives, and interaction protocols while addressing key challenges in the field. Investigate normal form and extensive form games, best response strategies, and Nash Equilibrium. Analyze the problem of goal alignment and the drawbacks of current interaction models. Gain valuable insights into this cutting-edge area of artificial intelligence research through clear explanations and thought-provoking questions.

Introduction
Motivation
Classical Game Theory
Reinforcement Learning
MultiGeneration Enforcement Learning
Task
Efficiency
Outline
Formulations Objectives
Interaction Protocol
Policy
Value
Questions
Normal Form Games
Extensive Form Games
What is the solution
The problem of goal
Best Response
Nash Equilibrium
Challenges
Two Questions
One Question
Cell Play
Interaction Model
Drawbacks