Auto Drive, Machine Learning


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Time: 9:30am – 12:30pm + 1:30pm to 4:30pm HKT
Duration: 21 hours
Mode: Online + Classroom


This course gives you an interesting and fun way to get started with reinforcement learning (RL) on DeepRacer. RL is an advanced machine learning (ML) technique that takes a very different approach to training models than other machine learning methods. Its super power is that it learns very complex behaviors without requiring any labeled training data. Our hands-on labs can help you learn , start training reinforcement learning models and test them in an exciting, autonomous car racing experience.

Course Outline:

Introducing the Game
  • Why DeepRacer?
  • About this Course

Module 1: How It Works
  • Reinforcement Learning
  • DeepRacer and AWS Services
  • Action Space
  • Discrete or Continuous Action Space
  • Reward Function
  • Reward Function Example
  • HyperParameters
  • Lab: Creating Your 1st DeepRacer Model
  • Lab: Evaluating Models with Simulation

Module 2: Advanced Reward Function
  • Cloning a Trained Model and Starting a New Training Pass
  • Input Parameters of the AWS DeepRacer Reward Function
  • Lab: Advancing the Model with Custom Reward Function
  • Lab: Evaluating the new Model

Module 3: WayPoint Racing
  • What is WayPoint?
  • Lab: Preparing WayPoints for your Agent
  • Lab: Creating the ML Model for WayPoints
  • Lab: Racing Line with WayPoint
  • Lab: Evaluating the WayPoint Model

Module 4: Advancing the Model with Log Analysis
  • Training Logs of Amazon SageMake and RoboMaker
  • JupyterLab and Jupyter Notebook
  • DeepRacer Log Analysis Tool

Bonus Lab: DeepRacer Training Log Analysis