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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
  • 8. MLPro-GT - Game Theory

Extension Hub

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

Appendices

  • A1 - Example pool
  • A2 - API reference
    • MLPro-BF - Basic Functions
    • MLPro-SL - Supervised Learning
    • MLPro-OA - Online Adaptivity
    • MLPro-RL - Reinforcement Learning
      • RL-ENV - Environments
      • RL-ENV-ADA - Environment models
      • RL-AGENTS - Agents
      • RL-TRAIN - Scenarios, training and tuning
      • Pool objects
    • MLPro-GT - Game Theory
    • 3rd Party Support
  • A3 - Project MLPro
MLPro Documentations
  • A2 - API reference
  • MLPro-RL - Reinforcement Learning
  • Edit on GitHub

MLPro-RL - Reinforcement Learning

  • RL-ENV - Environments
    • Reward
    • FctReward
    • EnvBase
    • Environment
  • RL-ENV-ADA - Environment models
    • AFctReward
    • SARSElement
    • SARSBuffer
    • EnvModel
  • RL-AGENTS - Agents
    • Policy
    • ActionPlanner
    • Agent
    • MultiAgent
  • RL-TRAIN - Scenarios, training and tuning
    • RLDataStoring
    • RLDataStoringEval
    • RLScenario
    • RLTrainingResults
    • RLTraining
  • Pool objects
    • Action Planners
    • Environments
    • Policies
    • SARS-Buffers
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