# AWS DeepRacer

Cloud service · AWS. Updated 2026-11-12. Page: https://blowstack.com/tools/cloud-services-hub/aws-deepracer

> AWS DeepRacer is a fully autonomous 1/18th scale race car designed to help developers get hands-on experience with reinforcement learning through a series of virtual and physical racing competitions.

## 8 key features of AWS DeepRacer

AWS DeepRacer is a fully autonomous 1/18th scale race car designed for experimenting with and learning reinforcement learning, featuring integration with AWS services, supports real-world racing competitions, and offers extensive educational and community resources.

1. **Fully Autonomous 1/18th Scale Race Car**: AWS DeepRacer is a fully autonomous 1/18th scale race car designed to test and experiment with reinforcement learning models. It provides a hands-on way to understand machine learning (ML) through autonomous racing.
2. **Reinforcement Learning Platform**: It is built on a reinforcement learning (RL) platform that allows developers of all skill levels to get hands-on with ML and RL. Users can train their models in a virtual environment before deploying them to their physical DeepRacer vehicles.
3. **AWS Integration**: AWS DeepRacer integrates with Amazon SageMaker for reinforcement learning model training, AWS RoboMaker for the racing simulator, Amazon Kinesis Video Streams for virtual simulation footage, Amazon S3 for model storage, and Amazon CloudWatch for log capture.
4. **Community Races**: Users can host races with DeepRacer on AWS to challenge colleagues or share races publicly with machine learning enthusiasts around the globe.
5. **Educational and Community Resources**: It comes equipped with a vast array of educational resources, including tutorials, workshops, and community forums, which help users of all levels to learn, share, and collaborate on reinforcement learning and autonomous racing projects.
6. **Clone Models for Continued Training**: Clone a trained model and tune its hyperparameters to continue training and improve performance.
7. **Physical Racing Event Management**: DeepRacer on AWS supports physical racing events with multiple tracks, timekeeping, lap validation, and combined leaderboards across tracks.
8. **Instance-Wide Usage Limits**: Administrators can set instance-wide compute and storage usage limits to help control operating budgets and variable costs.

## Where AWS DeepRacer is used

AWS DeepRacer serves as a versatile tool for education in reinforcement learning, corporate team-building, research in autonomous vehicle technologies, and for engaging a global community through competitions to build and share AI skills.

1. **Educational Tool for Reinforcement Learning**: AWS DeepRacer serves as a hands-on educational tool to teach developers, students, and technology enthusiasts about reinforcement learning (RL), an advanced machine learning (ML) technique. By participating in AWS DeepRacer leagues or experimenting in a controlled environment, users can better understand RL principles, experiment with algorithm tweaks, and see the real-time effects of their adjustments on the DeepRacer's performance.
2. **Corporate Team Building and Learning**: Companies can use AWS DeepRacer as an innovative team-building activity that also fosters learning in advanced technologies. By organizing internal competitions, employees engage in friendly rivalry while enhancing their skills in machine learning, AI, and teamwork, driving both personal development and potentially innovative business solutions.
3. **Research and Development in Autonomous Vehicles**: Researchers and technologists can utilize AWS DeepRacer as a platform for experimenting with and refining autonomous driving algorithms. This compact and controlled environment allows for rapid prototyping and testing of algorithms before scaling solutions to larger, more complex autonomous vehicle projects.
4. **Community Engagement and Skill Building**: AWS DeepRacer competitions, both virtual and physical, provide participants with a platform to engage with a global community of machine learning practitioners. These events encourage skill building in a competitive yet collaborative environment, fostering networking and knowledge sharing among participants from diverse backgrounds.

## How AWS DeepRacer is priced

AWS DeepRacer pricing includes a free trial for new users, on-demand pricing for training and evaluation hours beyond the free trial, and possible additional costs for event participation.

- **Free Trial**: AWS provides new AWS DeepRacer users with a limited-time free trial that includes 10 hours of model training and 5 hours of model evaluation per month, for the first 12 months after initial sign-up.
- **Usage-Based Training, Evaluation, and Storage**: DeepRacer on AWS charges based on usage to train or evaluate models and to store models.
- **Event Participation**: Participating in AWS DeepRacer events may incur additional costs depending on the event type and its duration. Some events are free, while others may charge a participation fee or require additional model training and evaluation hours that exceed the free tier limits.

## Services AWS DeepRacer works with

- **[Amazon SageMaker](https://blowstack.com/tools/cloud-services-hub/amazon-sagemaker)**: DeepRacer uses SageMaker to train reinforcement learning models in the cloud.
- **[AWS RoboMaker](https://blowstack.com/tools/cloud-services-hub/aws-robomaker)**: DeepRacer leverages RoboMaker for simulation environments to train and evaluate models.
- **[Amazon CloudWatch](https://blowstack.com/tools/cloud-services-hub/amazon-cloudwatch)**: CloudWatch is used for monitoring and logging training metrics and simulation jobs.
- **[Amazon S3](https://blowstack.com/tools/cloud-services-hub/amazon-s3)**: DeepRacer uses S3 to store training data, models, and simulation logs.
- **[AWS Lambda](https://blowstack.com/tools/cloud-services-hub/aws-lambda)**: Lambda can be used to automate processes, such as model evaluation and deployment triggers.
- **[AWS IoT Greengrass](https://blowstack.com/tools/cloud-services-hub/aws-iot-greengrass)**: IoT Greengrass provides a local runtime environment for deploying DeepRacer models to physical vehicles.
- **[AWS Identity and Access Management (IAM)](https://blowstack.com/tools/cloud-services-hub/aws-identity-and-access-management-(iam))**: IAM is used to manage access and permissions for DeepRacer resources and services.
- **[Amazon Kinesis Video Streams](https://blowstack.com/tools/cloud-services-hub/amazon-kinesis-video-streams)**: DeepRacer on AWS uses Amazon Kinesis Video Streams to stream video footage from virtual simulations.
- **[AWS CloudFormation](https://blowstack.com/tools/cloud-services-hub/aws-cloudformation)**: CloudFormation templates can automate the setup and configuration of resources required for DeepRacer.
- **[Amazon API Gateway](https://blowstack.com/tools/cloud-services-hub/amazon-api-gateway)**: DeepRacer on AWS exposes its backend operations through Amazon API Gateway REST endpoints. These endpoints route requests for managing models, training jobs, races, and other platform resources to Lambda handlers.
- **[Amazon CloudFront](https://blowstack.com/tools/cloud-services-hub/amazon-cloudfront)**: DeepRacer on AWS uses Amazon CloudFront to deliver its web-based user interface. CloudFront serves the static interface assets stored in an S3 bucket.
- **[Amazon Cognito](https://blowstack.com/tools/cloud-services-hub/amazon-cognito)**: DeepRacer on AWS uses Amazon Cognito user pools for registration, sign-in, and user group membership, and identity pools for federation and authorization. Cognito lifecycle triggers invoke Lambda functions to create profiles and assign new racers to the default group.
- **[Amazon DynamoDB](https://blowstack.com/tools/cloud-services-hub/amazon-dynamodb)**: DeepRacer on AWS stores structured platform data in an Amazon DynamoDB table, including user profiles, model metadata, training and evaluation jobs, race submissions, and leaderboards. The table also holds physical-event records such as tracks, runs, and lap times.
- **[Amazon EC2](https://blowstack.com/tools/cloud-services-hub/amazon-ec2)**: AWS DeepRacer uses Amazon EC2 indirectly through CloudFormation and SageMaker to provision and run the compute resources required for model training jobs.
- **[Amazon Elastic Container Registry (Amazon ECR)](https://blowstack.com/tools/cloud-services-hub/amazon-elastic-container-registry-(amazon-ecr))**: DeepRacer on AWS uses Amazon ECR to host the public container image for its Simulation Application. SageMaker instances use this image to run the simulation for model training and evaluation.
- **[Amazon EventBridge](https://blowstack.com/tools/cloud-services-hub/amazon-eventbridge)**: AWS DeepRacer API activity recorded by CloudTrail can be delivered to Amazon EventBridge. EventBridge rules can match the aws.deepracer source and specific API operation names to trigger notifications or automated processing.
- **[Amazon GuardDuty](https://blowstack.com/tools/cloud-services-hub/amazon-guardduty)**: DeepRacer on AWS uses Amazon GuardDuty to scan uploaded physical-car models for malware before they are optimized for deployment to a vehicle.
- **[Amazon Simple Queue Service (Amazon SQS)](https://blowstack.com/tools/cloud-services-hub/amazon-simple-queue-service-(amazon-sqs))**: DeepRacer on AWS uses an Amazon SQS FIFO queue to hold simulation jobs before dispatch. It also uses queues for background cleanup of deleted physical-event records and for handling failed model-packaging jobs.
- **[Amazon VPC](https://blowstack.com/tools/cloud-services-hub/amazon-vpc)**: AWS DeepRacer uses Amazon VPC networking resources to run its training jobs. Its account setup configures the permissions needed to access these resources.
- **[AWS AppConfig](https://blowstack.com/tools/cloud-services-hub/aws-appconfig)**: DeepRacer on AWS stores application-level settings in AWS AppConfig hosted configuration. These settings include usage quotas applied by the platform.
- **[AWS CloudTrail](https://blowstack.com/tools/cloud-services-hub/aws-cloudtrail)**: AWS CloudTrail records supported AWS DeepRacer API activity for auditing. It can also deliver those API-call events to EventBridge for rule-based processing.
- **[AWS IoT Core](https://blowstack.com/tools/cloud-services-hub/aws-iot-core)**: DeepRacer on AWS uses AWS IoT Core as a managed WebSocket publish/subscribe channel for live race updates, including leaderboard changes, evaluation progress, and participant notifications. It also carries countdown timer state to spectator displays and lap-crossing messages from Raspberry Pi timers to the timekeeping page.
- **[AWS Step Functions](https://blowstack.com/tools/cloud-services-hub/aws-step-functions)**: DeepRacer on AWS uses AWS Step Functions to orchestrate model training and evaluation jobs on SageMaker instances. Step Functions also coordinates the workflow that delivers optimized models to physical cars.
- **[AWS WAF](https://blowstack.com/tools/cloud-services-hub/aws-waf)**: DeepRacer on AWS uses AWS WAF to protect its API against common web attacks, including bot traffic and credential stuffing. Administrators can define custom rules suited to their deployment's traffic patterns.

## What is AWS DeepRacer

AWS DeepRacer offers an intriguing and engaging way for developers of all skill levels to get hands-on experience with reinforcement learning (RL), which is a type of machine learning. This immersive, 3D racing simulator provides users with a practical means to experiment with and understand RL by using it to autonomously control miniature cars, through models trained in a virtual environment. The entire setup of AWS DeepRacer is ingeniously designed to introduce the complexities and potentials of machine learning in a fun and approachable manner.

At the heart of AWS DeepRacer is the combination of a fully autonomous 1/18th scale race car and a cloud-based 3D racing simulator. The car itself is equipped with a variety of sensors that allow it to perceive its environment, and it operates based on models created, trained, and tested within the AWS cloud using reinforcement learning algorithms. This design choice abstracts away much of the complexity typically associated with deploying machine learning models, making AWS DeepRacer exceptionally accessible to a broad audience.

DeepRacer on AWS is available as an open-source product that customers deploy and operate within their own AWS accounts.

Since December 15, 2025, AWS DeepRacer as a service is no longer available in the AWS Console, and AWS DeepRacer devices are no longer available for purchase on amazon.com.

Training and evaluation jobs are constrained by service quotas and remain queued until capacity is available, which matters when sizing deployments for classes, workshops, or other periods of high demand.

The principle underlying reinforcement learning, which is central to AWS DeepRacer, involves teaching the model through rewards. Developers can program the car to navigate its way around a track by specifying which actions (such as accelerating, decelerating, or turning) lead to positive outcomes (such as staying on the track) and which actions lead to negative outcomes (such as crashing or going off track). Over time, through a process of trial and error within the simulated environment, the model learns to navigate courses successfully. AWS DeepRacer also capitalizes on the competitive spirit by hosting a global racing league. This competition allows developers to pit their trained models against those of other participants, fostering a community around machine learning and encouraging participants to refine their models.

The league functions as an excellent motivation for continuous learning and improvement in the field of reinforcement learning. Furthermore, AWS DeepRacer extends beyond just a learning tool and into research and development. Enterprises and researchers can use the platform's simplified approach to reinforcement learning for broader applications than just racing. This could include navigating real-world environments or optimizing decision-making processes in varied settings, showcasing the versatility and potential of reinforcement learning as facilitated by AWS DeepRacer in understanding and applying machine learning concepts practically.

In summary, AWS DeepRacer stands as a transformative educational tool that demystifies reinforcement learning through interactive, hands-on experience. It serves as a bridge between theoretical knowledge and practical application, offering a platform for learning, experimentation, and competition in the realm of machine learning.