MLPro Documentations
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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
6.1. Overview
6.2. Getting started
6.3. Environments
6.3.1. Developing custom environments
6.3.2. Reusing environments from the pool
6.3.2.1. Bulk Good Laboratory Plant (BGLP)
6.3.2.2. Multi-Cartpole
6.3.2.3. Grid World
6.3.2.4. Robot Manipulator on Homogeneous Matrix
6.3.2.5. Double Pendulum
6.3.2.6. 2D Collision Avoidance
6.4. Agents
6.5. Scenarios
6.6. Training and tuning
6.7. 3rd party support
7. MLPro-GT - Game Theory
Extension Hub
8. General information
9. Third-party extensions
Appendices
A1 - Example pool
A2 - API reference
A3 - Project MLPro
MLPro Documentations
6.
MLPro-RL - Reinforcement Learning
6.3.
Environments
6.3.2.
Reusing environments from the pool
Edit on GitHub
6.3.2.
Reusing environments from the pool
6.3.2.1. Bulk Good Laboratory Plant (BGLP)
6.3.2.2. Multi-Cartpole
6.3.2.3. Grid World
6.3.2.4. Robot Manipulator on Homogeneous Matrix
6.3.2.5. Double Pendulum
6.3.2.6. 2D Collision Avoidance