Ready, Set, AWS DeepRacer
It was a bright Friday afternoon in July in the lovely city of Boston, Massachusetts. We had close to 30 participants ready to race their cars. Sounds straight out of a Fast and Furious movie, right? What if I told you that these participants were actually racing autonomous vehicles on a virtual race track created on Amazon Sagemaker?
This project was the brainchild of Kristina, Pawan, and Sathwik. Earlier this year, they attended a workshop on reinforcement learning for autonomous vehicle racing conducted by Amazon. When they took the idea of conducting workshops in collaboration with Amazon Web Services (AWS) to Ram, the CEO of Colaberry, he was very enthusiastic. And that was what led me to create Colaberry – AWS Deepracer course on Deepracer for quite a few days along with a fellow intern, Manaswi.
We found that Deepracer can be an important tool to introduce people to the world of Machine Learning: they can see its real-world applications and use it in a fun way. The virtual vehicle applies the ML reward function by rewarding an autonomous vehicle for following the track properly and penalizing it for “bad” behaviors like going off track. This reward function utilizes Python programming, and advanced participants in the AWS Deepracer league even prepared their own codes using an exhaustive list of input parameters. For those unfamiliar with coding, the platform also had pre-created reward functions.
As an introductory workshop, we refrained from using complex code, instead explaining the pre-created codes. Advanced users or those seeking to participate in the community race the next day could use the advanced notebooks available. These notebooks provided a deeper understanding of hyperparameters, reward graphs, and other tips that can help a person succeed in the race.
Are you ready to race?
This post was written by Badri Yashwant Kuram, a Colaberry Intern.
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