DeepRacer: Optimization and Adaptation for Autonomous Racing via Deep Reinforcement Learning

Developed a Proximal Policy Optimization (PPO) approach for solving autonomous racing tasks in the AWS DeepRacer simulation environment. The approach employs a convolutional neural network architecture designed to process multi-modal sensor inputs, including stereo camera images and LIDAR distance measurements.