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  • MLPro - Elevate your machine learning journey

Welcome to MLPro

  • 1. Introduction
  • 2. Getting started
  • 3. News ticker

Basic Functions

  • 4. MLPro-BF - Basic Functions

Machine Learning

  • 5. MLPro-SL - Supervised Learning
  • 6. MLPro-OA - Online Adaptivity
  • 7. MLPro-RL - Reinforcement Learning
    • 7.1. Overview
    • 7.2. Getting started
    • 7.3. Environments
      • 7.3.1. Developing custom environments
      • 7.3.2. Reusing environments from the pool
        • 7.3.2.1. Bulk Good Laboratory Plant (BGLP)
        • 7.3.2.2. Multi-Cartpole
        • 7.3.2.3. Grid World
        • 7.3.2.4. Robot Manipulator on Homogeneous Matrix
        • 7.3.2.5. Double Pendulum
        • 7.3.2.6. 2D Collision Avoidance
    • 7.4. Agents
    • 7.5. Scenarios
    • 7.6. Training and tuning
    • 7.7. 3rd party support
  • 8. MLPro-GT - Game Theory

Extension Hub

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

Appendices

  • A1 - Example pool
  • A2 - API reference
  • A3 - Project MLPro
MLPro Documentations
  • 7. MLPro-RL - Reinforcement Learning
  • 7.3. Environments
  • 7.3.2. Reusing environments from the pool
  • Edit on GitHub

7.3.2. Reusing environments from the pool

  • 7.3.2.1. Bulk Good Laboratory Plant (BGLP)
  • 7.3.2.2. Multi-Cartpole
  • 7.3.2.3. Grid World
  • 7.3.2.4. Robot Manipulator on Homogeneous Matrix
  • 7.3.2.5. Double Pendulum
  • 7.3.2.6. 2D Collision Avoidance
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