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  • MLPro - Elevate your machine learning journey

Welcome to MLPro

  • 1. Introduction
  • 2. Getting started

Basic Functions

  • 3. MLPro-BF - Basic Functions

Machine Learning

  • 4. MLPro-SL - Supervised Learning
  • 5. MLPro-OA - Online Adaptivity
  • 6. MLPro-RL - Reinforcement Learning
  • 7. MLPro-GT - Game Theory
    • 7.1. Overview
    • 7.2. Getting started
    • 7.3. MLPro-GT-Native - Native games
      • 7.3.1. Player, Coalition, Competition
      • 7.3.2. Payoff
      • 7.3.3. Solvers
      • 7.3.4. Games
        • 7.3.4.1. Custom Games
        • 7.3.4.2. Games Pool
    • 7.4. MLPro-GT-DG - Dynamic games

Extension Hub

  • 8. General information
  • 9. Third-party extensions

Appendices

  • A1 - Example pool
  • A2 - API reference
  • A3 - Project MLPro
MLPro Documentations
  • 7. MLPro-GT - Game Theory
  • 7.3. MLPro-GT-Native - Native games
  • 7.3.4. Games
  • 7.3.4.2. Games Pool
  • Edit on GitHub

7.3.4.2. Games Pool

  • 7.3.4.2.1. 2P Prisoners’ Dilemma
  • 7.3.4.2.2. 3P Prisoners’ Dilemma
  • 7.3.4.2.3. Rock, Paper, Scissors
  • 7.3.4.2.4. 3P Supply and Demand
  • 7.3.4.2.5. 3P Routing Problems
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