Appendix

Intermediate Game Theory

Normal-Form Games

Multi-Agent Reinforcement Learning Foundations And Modern Approaches

'3 Games: Models of Multi-Agent Interaction' up to and excluding '3.3 Stochastic Games'

1800 Words (Technical)

Extensive-Form Games

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The following introduces extensive form games which model scenarios with temporality.

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

'5.1 Perfect-information extensive-form games' up to and excluding '5.1.3 Subgame-perfect equilibrium'

Prerequisites

Normal-Form Games

1500 Words (Technical)

Extensive-Form Games

Repeated games

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The following is a rigorous introduction to repeated games using the extensive form representation of games.

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

'6.1 Repeated games' excluding '6.1.3 “Bounded rationality": repeated games played by automata'

Prerequisites

Extensive Form Games

2300 Words (Technical)

Repeated Games

Solution Concepts

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This resource explains solution concepts. The chapter '3.4 Further solution concepts for normal-form games' includes descriptions of many important solution concepts and can be used as reference material.

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

'3.3 Analyzing games: from optimality to equilibrium' up to and excluding '3.3.3 Finding Nash equilibria'

Prerequisites

Normal-Form Games

1400 Words (Technical)

Solution Concepts

Next is another look at solution concepts beyond the context of normal-form games. The chapter '4 Solution Concepts for Games' also includes descriptions of many important solution concepts and can be used as reference material.

Multi-Agent Reinforcement Learning Foundations And Modern Approaches

'4 Solution Concepts for Games' up to and including '4.2 Best Response'. And '4.4 Nash Equilibrium'

Prerequisites

Markov Games

2900 Words (Technical)

Solution Concepts

Markov Games

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Note that Markov games and stochastic games are different terms for the same thing.

Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations

'6.2 Stochastic games' excluding '6.2.3 Computing equilibria'

1100 Words (Technical)

Markov Games

Multi-Agent Reinforcement Learning Foundations And Modern Approaches

'3.3 Stochastic Games' and '3.4 Partially Observable Stochastic Games'

3000 Words (Technical)

Markov Games

Fictitious Play and Regret Matching

From 1:02

12 mins (Technical)

Fictitious Play

Regret Matching

In the following resource you can skip most of '2.4 Worked Example: Rock-Paper-Scissors'.

An Introduction to Counterfactual Regret Minimization

'2 Regret in Games'

3800 Words (Technical)

Regret Matching

Machine Learning

But What Is a Neural Network?

All parts

3600 Words (Technical)

Basic Machine Learning

A short introduction to machine learning

All parts

2300 Words (Technical)

Basic Machine Learning

Reinforcement Learning

Reinforcement Learning: Machine Learning Meets Control Theory

All parts

30 mins

Reinforcement Learning

Hugging face Introduction to Deep Reinforcement Learning

Unit 1. Introduction to Deep Reinforcement Learning

Reinforcement Learning

Q-Learning

Hugging face Introduction to Q-Learning

Unit 2. Introduction to Q-Learning

Prerequisites

Reinforcement Learning

Q-Learning

Proximal Policy Optimisation

Hugging face Introduction to Deep Reinforcement Learning

Unit 8. Part 1 Proximal Policy Optimization (PPO)

Prerequisites

Reinforcement Learning

Proximal Policy Optimization

Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning Foundations And Modern Approaches

'1 Introduction' up to and excluding '1.4 Challenges of MARL'

Prerequisites

Reinforcement Learning

4400 Words (Technical)

MARL

Large Language Models

Intro to Large Language Models

Up until 21:05

21 mins

LLM

LLM-Based Agents

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

2.5 Agentic LLMs Pose Novel Risks

Prerequisites

LLM

2600 Words

LLM-Agents