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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

Extension Hub

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

Appendices

  • A1 - Example pool
    • MLPro-BF - Basic Functions
    • MLPro-SL - Supervised Learning
    • MLPro-OA - Online Adaptivity
    • MLPro-RL - Reinforcement Learning
    • MLPro-GT - Game Theory
      • Dynamic Games
        • Howto GT-DG-001: Train Multi-Player in Potential Games
        • Howto GT-DG-002: Train Multi-Player in Stackelberg Games
        • Howto GT-DG-003: Train Multi-Player with State-based Potential Games on the BGLP
      • Native GT
        • Howto GT-Native-001: 2P Prisoners’ Dilemma
        • Howto GT-Native-002: 3P Prisoners’ Dilemma
        • Howto GT-Native-003: Rock, Paper, Scissors
        • Howto GT-Native-004: 3P Supply and Demand
        • Howto GT-Native-005: 3P Routing Problems
  • A2 - API reference
  • A3 - Project MLPro
MLPro Documentations
  • A1 - Example pool
  • MLPro-GT - Game Theory
  • Dynamic Games
  • Edit on GitHub

Dynamic Games

  • Howto GT-DG-001: Train Multi-Player in Potential Games
  • Howto GT-DG-002: Train Multi-Player in Stackelberg Games
  • Howto GT-DG-003: Train Multi-Player with State-based Potential Games on the BGLP

Native GT

  • Howto GT-Native-001: 2P Prisoners’ Dilemma
  • Howto GT-Native-002: 3P Prisoners’ Dilemma
  • Howto GT-Native-003: Rock, Paper, Scissors
  • Howto GT-Native-004: 3P Supply and Demand
  • Howto GT-Native-005: 3P Routing Problems
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