🏎️ AWS DeepRacer Autonomous Racing Model

Developed an AWS DeepRacer model using Python and the Proximal Policy Optimization (PPO) algorithm, leveraging TensorFlow to train and fine-tune a deep reinforcement learning model. Designed a custom reward function and optimized hyperparameters to improve policy learning and navigation performance. Utilized AWS infrastructure for scalable training and deployment.

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🚀 Features

• Custom Reward Function – Designed to incentivize optimal racing strategies and improve lap times.
• Hyperparameter Optimization – Fine-tuned parameters to enhance model convergence and performance.
• AWS Integration – Leveraged AWS services for scalable training and deployment of the reinforcement learning model.
• Simulation and Real-World Deployment – Trained in a simulated environment with deployment capabilities to the AWS DeepRacer car for real-world testing.

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🛠️ Technologies Used

Component	Technology
Programming Language	Python
Machine Learning Library	TensorFlow
Algorithm	Proximal Policy Optimization (PPO)
Cloud Services	AWS SageMaker, AWS RoboMaker, Amazon S3
Deployment	AWS DeepRacer Console

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▶️ How to Use

Prerequisites

• AWS Account – Access to AWS services such as SageMaker, RoboMaker, and the DeepRacer console.
• AWS DeepRacer Vehicle – Optional, for deploying and testing the model in a physical environment.

Training the Model

1. Access AWS DeepRacer Console: Navigate to the AWS DeepRacer console to create and manage your models.

2. Define the Reward Function: Utilize the custom reward function provided in the notebooks/aws_deepracer_part_2.ipynb notebook to guide the agent's learning process.

3. Configure Hyperparameters: Set the hyperparameters as detailed in the notebook to optimize training performance.

4. Initiate Training: Start the training job in the AWS DeepRacer console, monitoring progress and performance metrics.

Evaluating and Deploying the Model

1. Evaluate Performance: After training, assess the model's performance within the simulated environment provided by AWS RoboMaker.

2. Download the Model: Once satisfied with the performance, download the trained model files.

3. Deploy to AWS DeepRacer Vehicle: Upload the model to the physical AWS DeepRacer car for real-world testing and validation.

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🎥 Demo

Click the image above to watch a short demonstration of the AWS DeepRacer model navigating the track.

🙌 Acknowledgments

• AWS DeepRacer Community – For resources and support in developing and refining reinforcement learning models.
• OpenAI – For advancements in reinforcement learning algorithms, including the development of PPO.
• TensorFlow – For providing a robust platform for implementing and training deep learning models.
• AWS Educate Program – For access to cloud resources and services that facilitated the development of this project.