Get hands-on with AWS DeepRacer and compete in the AWS DeepRacer League - AIM206-R - New York AWS Summit

1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Get hands-on with AWS DeepRacer and compete in the AWS DeepRacer League Brien Blandford Partner Solutions Architect Amazon Web Services A I M 2 0 6 - R

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Agenda • AWS DeepRacer origin • RL for the Sunday driver • Virtual simulator • Rubber meets the road • Under the hood

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 1/18 scale autonomous race car AWS DeepRacer:An exciting wayfor developers to get hands-on experience with machine learning Global Racing LeagueVirtual simulator, to train and evaluate

6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS DeepRacer League, race for prizes and glory The world’s first global, autonomous racing league www.deepracerleague.com Keen on setting up a race in your company? Please contact us.

9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Reinforcement learning in the broader AI context Reinforcement learning Supervised learning Unsupervised learning

13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T The reward function The reward function incentivizes particular behaviors and is at the core of reinforcement learning

15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Incentivizing centerline behavior 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 S 2 2 2 2 2 2 G = 2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 8.6 9.5 8.5 7.5 6.3 5.0 3.5 1.9 S 10.4 9.4 8.2 6.9 5.4 3.8 G = 2 8.6 9.5 8.5 7.5 6.3 5.0 3.5 1.9 Discount per step 0.9

17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T RL algorithms: Vanilla policy gradient * Image Source: Landscape image is CC0 1.0 public domain J()New weights New weights 0.4 ± 𝛿 0.3 ± 𝛿

19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Method Supervised learning How it works Expert driver controls a real world car, that has a camera. Save the images from the camera as inputs and corresponding driving actions (speed and steering angle) as outputs. Train a model. Result Provide state(image) into model and receive driving action RL vs. other approaches for robotic racing Method Reinforcement learning How it works Virtual agent repeatedly interacts with a simulated environment and logs experience (image, action, new state, reward). Experience is used to train a model, and new model is used to get more experience. Result Provide state(image) into model and receive driving action

21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Lab 0 – AWS DeepRacer service resource creation Objective: Set up your account resources to get you to the races! Time: 5 min. 1. Find the lab content here: https://github.com/aws-samples/aws-deepracer-workshops/ 2. Navigate to: Workshops/2019-AWSSummits-AWSDeepRacerService/Lab0_Create_resources

22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS Cloud AWS DeepRacer NAT gateway VPC AWS DeepRacer Models Simulation video Metrics AWS DeepRacer simulator architecture

25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Track components Track center Track wall Track surface, a.k.a. on-track Field, a.k.a. off-track Track boundaries

26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Coordinate system and track waypoints Outer boundary waypoints Track center waypoints Inner boundary waypoints X Y Track width Car direction

30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS DeepRacer League, race for prizes and glory The world’s first global, autonomous racing league www.deepracerleague.com

32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Lab 1 – AWS DeepRacer service Objective: Build your first AWS DeepRacer RL model Time: 50 min. 1. Find the lab content here: https://github.com/aws-samples/aws-deepracer-workshops/ 2. Navigate to: Workshops/2019-AWSSummits-AWSDeepRacerService/Lab1

33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS DeepRacer: Driven by reinforcement learning Want to learn more? Learn how to build a reinforcement learning model, and find tips and tricks about how to tune those models to climb the League leaderboard in a digital training course for reinforcement learning and AWS DeepRacer This 90-minute course is available at no cost, has six self-guided chapters, and helps you prepare to compete in the AWS DeepRacer League https://www.aws.training/learningobject/wbc?id=32143

35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS DeepRacer car specifications Car 18th scale 4WD with monster truck chassis CPU Intel Atom Processor Memory 4-GB RAM Storage 32 GB (expandable) Wi-Fi 802.11ac Camera 4 MP camera with MJPEG Drive battery 1,000 mAh lithium polymer Compute battery 13,600 mAh USB-C Sensors Integrated accelerometer and gyroscope Ports 4x USB-A, 1x USB-C, 1x Micro-USB, 1x HDMI Software Ubuntu OS 16.04.3 LTS, Intel OpenVINO toolkit, ROS Kinetic

36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T ROS msg node Stored file ROS nodes Web server publisher Model optimizer Video M- JPEG Web server video Inference results Autonomous drive Control node Optimized model Media engine Camera Model Inference engine Manual drive Navigation node Servo & Motor AWS DeepRacer software architecture

37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Simulation-to-real domain transfer Sim-to-real challenge Train model using simulated images, but the race car using the images the car experiences in the real world Strategies Environment control Domain randomization Modularity and abstraction

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