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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
    • 7.4. 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
  • Edit on GitHub

7. MLPro-GT - Game Theory

  • 7.1. Overview
  • 7.2. Getting started
    • 7.2.1. MLPro-GT-Native - Native Games
    • 7.2.2. MLPro-GT-DG - Dynamic Games
  • 7.3. MLPro-GT-Native - Native games
    • 7.3.1. Player, Coalition, Competition
      • 7.3.1.1. Player
      • 7.3.1.2. Coalition
      • 7.3.1.3. Competition
    • 7.3.2. Payoff
      • 7.3.2.1. Payoff Matrix
      • 7.3.2.2. Transfer Function
    • 7.3.3. Solvers
      • 7.3.3.1. Custom Solvers
      • 7.3.3.2. Solvers Pool
    • 7.3.4. Games
      • 7.3.4.1. Custom Games
      • 7.3.4.2. Games Pool
  • 7.4. MLPro-GT-DG - Dynamic games
    • 7.4.1. Game Boards
      • 7.4.1.1. Custom Game boards
      • 7.4.1.2. Reusing RL Environments
      • 7.4.1.3. Game board Pool
    • 7.4.2. Players
      • 7.4.2.1. Custom Policies
      • 7.4.2.2. Players Pool
    • 7.4.3. Games
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