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Writer's pictureXuebin Wei

Mastering AWS DeepRacer: Reinforcement Learning for Training, Racing, and Self-Driving

Updated: Oct 26


 


In this video, we're diving into the exciting world of reinforcement learning through AWS DeepRacer—a hands-on way to experiment with self-driving models on virtual and real-world cars!



Step 1: Introduction to DeepRacer and Reinforcement Learning

Reinforcement learning (RL) trains a model, or “agent” (our DeepRacer car), to interact with its environment by rewarding good behavior and correcting mistakes. Unlike other machine learning methods, RL learns through trial and error, improving with each run.


Step 2: Getting Started with DeepRacer

AWS offers two car types: a single-camera model and an advanced stereo camera with lidar. The stereo camera and lidar help with spatial awareness, so this is a great option for working on complex races or obstacle courses. Setting up either car is straightforward, and AWS guides cover car assembly and calibration.


Step 3: Training Models and Customizing Reward Functions

In the AWS DeepRacer console, we’ll configure the reward function to shape how our car navigates the track. The reward function is critical for keeping a steady course or avoiding obstacles. With the console’s “Garage” feature, we can customize the car’s response to different conditions and help it learn.


Step 4: Setting Up a Virtual Race

Once our models are trained, it’s race time! We’ll set up a virtual race environment where students can enter their models to compete. With AWS DeepRacer's shared racing platform, two students join the race, each with unique model configurations. The models are tested on track speed, stability, and cornering control, adding a fun and competitive edge to the learning experience.


Step 5: Testing Models on the Real Car

Finally, we’ll deploy the trained models to real DeepRacer cars. Students connect their vehicles, load models, and take their self-driving cars for a test drive on an actual track. We’ll see how each model handles real-world conditions, observing the results of our reward functions and adjustments.




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