To make the vehicles autonomous the concept of reinforcement learning is implemented from artificial intelligence and machine learning. Reinforcement learning is one of the three learning paradigms offered by AI ML which is used to take the most suitable action by the associated machine to generate the most optimized output.
Amazon DeepRacer is making a lot of hype in the market. Let us introduce you to the hidden treasures of DeepRacer AWS, look at the points that we are going to cover in the blog post, and let’s get started.
Table of Contents
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Introduction to AWS DeepRacer
This is the very first section of the blog, where we will introduce the topic to you. After this section, you will have a basic understanding of Amazon’s DeepRacer. It is an interactive way of learning about artificial intelligence and machine learning.
To cut a long story short, DeepRacer AWS refers to a type of integrated learning system that uses the concept of reinforcement learning to develop and build autonomous driving applications and small scaled autonomous driving vehicles.
- AWS’s DeepRacer allows users to experiment and gain hands-on experience with the next-generation autonomous vehicle technology.
- Diving deeper into the topic it can also be defined as the self-driving 1/18th scale car that is designed to experiment and test racing cars on physical tracks.
- In this, a user trains a machine learning model using a reinforcement learning paradigm using Amazon’s DeepRacer console, then specifies a simulated environment for testing while choosing an algorithm, and at the end customization of the reward function.
- Three types of services are used for its implementations, namely, SageMaker, Amazon S3, and AWS RoboMaker.
- SageMaker acts as a machine learning platform for AWS, RoboMaker is used to create a simulated environment for testing, and Amazon S3 is utilized as cloud storage.
In the previous section, we introduced you to AWS DeepRacer. In the coming-up portion, we are going to discuss various types of terminologies by which you will be able to have a deeper understanding of Amazon DeepRacer.
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AWS DeepRacer Concepts and Terminologies
Welcome to the next section of the blog, here we will be revealing various concepts and terminologies associated with Amazon Web Service DeepRacer. These concepts will help you to understand Amazon Web Service DeepRacer in a better way. Also, it will help you to build a strong foundation in the concerned domain.
- DeepRacer Simulator – It is a type of virtual environment specifically developed using AWS RoboMaker, the platform was developed for testing, training, evaluating, and optimizing the DeepRacer models.
- Amazon DeepRacer Vehicle – Three types of vehicles exist in Amazon DeepRacer, these vehicles are used on the physical tracks by the users. These vehicles are programmed using the concepts of machine learning and artificial intelligence. These vehicles are used to compete in AWS DeepRacer Leagues –
- Virtual Race Car – Virtual Race Car is one of the types of vehicles present in the domain. These types of vehicles are highly customizable These vehicles are used to participate in AWS DeepRacer Virtual Circuits Races. Users can earn rewards from these virtual circuit races.
- Evo Device – It is a type of original device that comes with an optional sensor kit that includes LIDAR (Light Detection and Ranging) and additional cameras. This optional sensor kit allows the vehicle to detect various objects around it.
- Original DeepRacer Device – It is the original AWS DeepRacer device, it is a physical 1/18th scale model car. It includes a mounted camera on its top and an onboard computing model. The model cars are powered by dedicated pairs of batteries.
- AWS DeepRacer Track – Track refers to the types of a course or paths on which the DeepRacer cars drive. These tracks can exist in three ways: The real world, the simulated environment, and the physical environment. DeepRacer tracks are provided by AWS DeepRacer Leagues on which virtual circuit races are carried out.
- Reward Function – The reward function is a key element that is implemented in reinforcement learning. It is a type of special algorithm that is used to let the agent know about the actions to be performed on the results. There are three types of interpretations on which this special type of algorithm provides results –
- Good Outcome (Reinforcement should be done)
- Neutral Outcome (No action required)
- Bad Outcome (The outcome should be discouraged)
- Experience Episode – It refers to the time interval in which the agents from the reinforcement model collect the user’s data as training data. Here episodes with different time intervals and lengths are collected that constitute the training data.
You should also be familiar with the concepts of various machine learning frameworks and paradigms, algorithm optimization, neural networks, and hyperparameters.
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The concepts mentioned in the previous section should be treated as prerequisites to the topic and should be thoroughly studied before. In the next section of the blog, we are going to discuss the console it owns.
AWS DeepRacer Console
Console in various domains of computer science simply means a graphical user interface used to interact with the system associated with the concerned domain. Amazon DeepRacer Console is one of them, it refers to a graphical user interface used to interact with the services provided by AWS DeepRacer.
- The console can also be used for the training and testing of reinforcement learning models. This creation and evaluation are done on the simulator provided by the console.
- You can download various previously trained models and implement them on your autonomous vehicles to drive them on the physical tracks. It also allows the cloning of the reinforced models for increasing the efficiency of the model.
- The efficiency of the model can be increased by tuning the hyperparameters. Due to this optimization, the performance of the models also increased.
- Using SageMaker and AWS RoboMaker helps the users to develop their tracks in a simulated environment.
Moving forward with the blog, let’s have a look at our final topic of the blog, DeepRacer Leagues.
AWS DeepRacer Leagues and Competitions
Welcome to the final section of the blog. As mentioned before we are going to have a look at Amazon DeepRacer Leagues and AWS DeepRacer Competition. These are nothing but competitions where users present their models built on their ML skills for participation.
In a nutshell, it is a type of event in Amazon’s DeepRacer termed as League or Competition. These events act like real-life racing events with starting and ending dates. Users design their vehicles and participate in the event or design their structure of the competition and host the competitions sponsored by Amazon Web Service Leagues.
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Congratulations on making it to the finals of the blog post. In the blog, we discussed in-depth concepts related to AWS DeepRacer. It is a type of service offered by Amazon Web Services that users use to design ML-based projects and compete in various competitions hosted by community members.
Learning about AWS DeepRacer and implementing the concepts is a great way to enhance your machine learning and artificial intelligence skills. By this, we end our journey I hope you learned something new.
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