AWS launches the first global autonomous racing league, open to everyone
Developers train and race on simulated tracks or in-person with 1/18th scale cars driven by reinforcement learning in quest of the coveted DeepRacer Cup
SEATTLE--(BUSINESS WIRE)--Nov. 29, 2018-- Today at AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ:AMZN), launched the AWS DeepRacer League (DRL), the first global, autonomous racing league, open to everyone. Starting in early 2019, 20 AWS Summits worldwide will host a DeepRacer series tournament where anyone can compete in a race with their 1/18th scale AWS DeepRacer car driven by a reinforcement learning model built in Amazon SageMaker. Contestants can compete in as many events around the world as they wish, and the winners of each stage, plus the top 10 points scorers across the races, will qualify for the DeepRacer League Cup, held at re:Invent 2019 in Las Vegas, Nevada. Racers can also compete in virtual events and tournaments throughout the year by entering time trials on special tracks in the AWS DeepRacer simulator, available in the AWS Management Console. As with the physical events, winners and top points scorers in the virtual race circuit will advance to the DeepRacer League Cup at re:Invent 2019. To learn more about the DeepRacer League, visit: https://aws.amazon.com/deepracer.
The inaugural DRL event took place at this year’s AWS re:Invent conference in accelerated form over a duration of 22 hours. Starting on Wednesday afternoon, thousands of developers seized the chance to learn about reinforcement learning powered by Amazon SageMaker in workshops where they were also the first customers to receive AWS DeepRacer cars. At a specially-constructed racing area in the MGM Grand Garden Arena dubbed the “AWS DeepRacer MGM Speedway,” developers tested their reinforcement learning models and had their lap times with their cars recorded onto a Speedway Leaderboard. The fastest times advanced to the final where Rick Fish, co-founder of Jigsaw XYZ, from London, England, emerged victorious, taking the DRL Cup with a winning lap time of 51:50.
“Until now, developers interested in experimenting with reinforcement learning had to study academic papers and cobble together models with limited help. AWS DeepRacer and the DeepRacer League gives them the opportunity to discover reinforcement learning in a hands-on fashion and then proceed to build, train, and tune reinforcement learning models and deploy them into their autonomous model racing cars,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning. “By removing common challenges associated with reinforcement learning, giving developers the chance to have some fun, and providing them a complete autonomous model racing car along with AWS machine learning services like Amazon SageMaker, we are putting every developer in position to experiment with reinforcement learning and machine learning.”
Reinforcement learning is a powerful type of deep learning capable of optimizing decisions in complex environments without requiring any labeled training data in order to achieve a long term goal. With reinforcement learning’s steep learning curve and blockers for adoption, AWS’s introduction of the AWS DeepRacer and the DeepRacer League is one more step in AWS’s mission of putting machine learning and reinforcement learning into the hands of everyday developers.
“When I first heard the announcement about AWS DeepRacer in the keynote, I was totally pumped and thought it was a great way to get people interested and started in reinforcement learning. It’s a field with an incredibly hard barrier of entry, and it poses a mental blocker, but AWS DeepRacer and DRL really opens it up for people,” said the winner of the first DRL Cup, Rick Fish, co-founder of Jigsaw XYZ. “Amazon SageMaker RL, the pre-built models, and available frameworks made everything really accessible, such that in less than a day I was able to have this fantastic outcome. As someone who has never worked with reinforcement learning before, I wasn’t expecting to qualify for the finals at all – I thought someone was joking with me when I got the call! The whole experience was good fun, and I haven’t even begun to scratch the surface in terms of the service’s capabilities. I’m excited to personalize my car further, and I look forward to exploring SageMaker RL and AWS DeepRacer more.”
AWS DeepRacer cars can be pre-ordered today on Amazon.com for delivery in 2019, just in time for the opening of the DeepRacer League in the new year. In the meantime, developers can get started building and training reinforcement learning models today on the Amazon SageMaker RL simulator. To find out more about the DeepRacer League, visit https://aws.amazon.com/deepracer.
About AWS Machine Learning
With an extensive portfolio of services at all three layers of the technology stack, more customers reference using AWS for machine learning than any other provider. For advanced developers and scientists who are comfortable building, tuning, training, deploying, and managing models themselves, AWS offers P2 and P3 instances at the bottom of the stack—which provide up to six times better performance than any other GPU instances available in the cloud today—together with AWS’s deep learning AMI that embeds all the major frameworks, such as TensorFlow and MXNet. At the middle layer of the stack, organizations that want to use machine learning in an expansive way can leverage Amazon SageMaker, a fully managed service that removes the heavy lifting, complexity, and guesswork from each step of the machine learning process, empowering everyday developers and scientists to successfully use machine learning. Amazon SageMaker can also be used with AWS DeepLens, a deep-learning enabled wireless video camera that pairs an HD camera developer kit with a set of sample projects to help developers learn machine learning concepts. At the top layer of the stack, AWS provides solutions, such as Amazon Rekognition for deep-learning based video and image analysis, Amazon Polly for translating text to speech, Amazon Lex for building conversations, Amazon Transcribe for converting speech to text, Amazon Translate for translating text between languages, and Amazon Comprehend for understanding relationships and finding insights within text. Along with this broad range of services and devices, customers are working alongside Amazon’s expert data scientists in the AWS ML Lab to implement real-life use cases.
For over 12 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 125 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 57 Availability Zones (AZs) within 19 geographic regions around the world, spanning the US, Australia, Brazil, Canada, China, France, Germany, India, Ireland, Japan, Korea, Singapore, and the UK. AWS services are trusted by millions of active customers around the world—including the fastest-growing startups, largest enterprises, and leading government agencies—to power their infrastructure, make them more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.
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Source: Amazon Web Services, Inc.