06. Demo Part 3: Reinforcement Model Versatility

Part 3: Demo

Exploring Reinforcement Learning with Milk Consumption Case

Learn how to apply reinforcement learning using custom datasets. This illustration uses an artificial dataset to analyze if milk consumption leads to discomfort.

Main Steps Covered:

• Creating a Custom Environment:

  • A custom environment is developed to simulate milk consumption effects, structured for reinforcement learning.
  • Initialization involves setting up variables and action/observation spaces.

• Dataset Generation:

  • Artificial dataset creation to simulate random milk consumption and associated discomfort over 100 days.
  • Consumption above five ounces induces discomfort.

• ML Model Training:

  • Utilize DQN from the Gymnasium library with a training dataset.
  • The model goes through 10,000 learning steps.

• Testing the Model:

  • Evaluate the model's accuracy by comparison against a testing dataset.
  • The model achieved approximately 61% accuracy in predicting discomfort.

• Visual Analysis:

  • Use plots showing milk consumption over time and model prediction accuracy.

Key Takeaway:

Understanding customized dataset generation and environment creation aids in effectively training reinforcement learning models, providing insights into model learning dynamics.