AWS DeepRacer – Overview

Recently I had the privilege of attending the AWS Re:Invent 2018 conference in Las Vegas. Among the hundreds of announcements, there was one that particularly spoke to my passions of reinforcement learning and robotics.

The AWS DeepRacer!

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I was one of the lucky few that got into the AWS DeepRacer workshops where we were introduced to the technology in the service as well as interacting with the yet to be released DeepRacer console. We got to train our own reinforcement learning model and download it to a USB drive. And at the end of the workshop we got told we would be getting the AWS DeepRacer car for free!! 

What is the AWS DeepRacer?

Here is what AWS said it is:
“AWS DeepRacer is the fastest way to get rolling with machine learning, literally. Get hands-on with a fully autonomous 1/18th scale race car driven by reinforcement learning, 3D racing simulator, and global racing league.”

For more information, follow this link: https://aws.amazon.com/deepracer/

Here is what I think it is:
DeepRacer is not just an RC car with a DeepLens glued on top. It is an end to end machine learning and robotics project that allows you to learn about multiple disciplines of entire technology stacks. Reinforcement learning is just the tip of the iceberg.

What is the DeepRacer League?

A race in the simulation world that spills out to the physical world held at each AWS Summit throughout 2019. It’s also a way of enticing people to play with the product and touching on their competitive nature. The cars race via time trials and only one car in on the track at a time.

Overview of DeepRacer

The astonishing thing about this product is the sheer number of teams that must have been involved in getting it to release. Here is a list off the top of my head of the technologies that went into making this.

• Hardware Development and product design
• Robotics
  • Gazebo3d Physics Simulation engine
  • 3Dmodeling of the car and track including all the physics like inertia and joint configurations
  • RobotOperating System application design and implementation
• Development
  • Programming in C++, Python and more
  • Web Development
• CloudComputing, utilizing a large number of AWS services
• Operating system automation and scripting of Ubuntu deployments
• MachineLearning
  • SagemakerServices to automate the creation of models
  • Intel RL Coach platform which is a collection of RL algorithms optimized for Intel hardware
  • Utilizing and creating OpenAI Gym environments
  • An understanding of the Proximal Policy Optimization RL algorithm
  • nowledge and application of the bleeding edge domain optimization techniques in allowing a model trained in a simulation to be applied on a real physical robot

And I’m sure there are others. It is mind-blowing the amount of technology here. I have tried to break down the DeepRacer into technology stacks to show this.

As of the writing of this post, AWS DeepRacer Console is yet to be in live preview, and the cars are on Pre-Order for an early 2019 delivery.

If you do eventually get your hands on one of the real cars, you may be thinking, how do I use my autonomous vehicle when the service is still not available? Well, there are ways…

In the next few posts, we will look at getting started with the DeepRacer,  including a few lessons learned in setting up the real car, as well as setting up RoboMaker environments to allow for training our own model.