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v0.8.1

1 Introduction

  • 1.1 What is MLPro?
  • 1.2 Getting Started
  • 1.3 Architecture
  • 1.4 Dependencies

2 MLPro-BF – Basic Functions

  • 2.1 Elementary Functions
  • 2.2 Mathematics
  • 2.3 Machine Learning

3 MLPro-SL - Supervised Learning

  • 3.1 Overview

4 MLPro-RL – Reinforcement Learning

  • 4.1 Overview
  • 4.2 Environments
  • 4.3 Agents
  • 4.4 RL-Scenarios
  • 4.5 Training
  • 4.6 3rd Party Support

5 MLPro-GT – Game Theory

  • 5.1 Overview
  • 5.2 Players
  • 5.3 Game Boards

6 MLPro-UI – Interactive ML

  • 6.1 UI Framework SciUI

Appendix 1: Examples

  • Basic Functions
  • Reinforcement Learning
    • Howto 01 - (RL) Types of reward
    • Howto 02 - (RL) Run agent with own policy with gym environment
    • Howto 03 - (RL) Train agent with own policy on gym environment
    • Howto 04 - (RL) Run multi-agent with own policy in multicartpole environment
    • Howto 05 - (RL) Train multi-agent with own policy on multicartpole environment
    • Howto 10 - (RL) Train using SB3 Wrapper
    • Howto 11 - (RL) Wrap mlpro Environment class to gym environment
    • Howto 12 - (RL) Wrap mlpro Environment class to petting zoo environment
    • Howto 13 - (RL) Comparison Native and Wrapper SB3 Policy
    • Howto 14 - (RL) Train UR5 with SB3 wrapper
    • Howto 15 - (RL) Train Robothtm with SB3 Wrapper
  • Game Theory
  • User Interface

Appendix 2: API Reference

  • Core Functions
  • Wrappers
  • Pool Objects
  • Templates

Appendix 3: Project MLPro

  • Release Notes
  • Papers
  • Contribution
MLPro Documentations
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  • Reinforcement Learning
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Reinforcement Learning

We provide some examples of MLPro’s RL funcionalities implementation, which is available on our GitHub file.

  • Howto 01 - (RL) Types of reward
  • Howto 02 - (RL) Run agent with own policy with gym environment
  • Howto 03 - (RL) Train agent with own policy on gym environment
  • Howto 04 - (RL) Run multi-agent with own policy in multicartpole environment
  • Howto 05 - (RL) Train multi-agent with own policy on multicartpole environment
  • Howto 10 - (RL) Train using SB3 Wrapper
  • Howto 11 - (RL) Wrap mlpro Environment class to gym environment
  • Howto 12 - (RL) Wrap mlpro Environment class to petting zoo environment
  • Howto 13 - (RL) Comparison Native and Wrapper SB3 Policy
  • Howto 14 - (RL) Train UR5 with SB3 wrapper
  • Howto 15 - (RL) Train Robothtm with SB3 Wrapper
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