Teaching Toy Car to Self-drive in a Game Environment using Reinforcement Learning

This research paper explores the use of reinforcement learning (RL) to teach a toy car to self-drive in a game environment. The study focuses on the development of an RL-based algorithm that enables the toy car to learn the optimal driving behavior through trial-and-error experience. The game environment provides a simulated environment for the toy car to navigate, and the RL algorithm rewards the car for taking the right actions while penalizing it for taking the wrong ones. The study also examines the effects of different reward structures and hyperparameters on the performance of the RL algorithm. The results of the study demonstrate that the toy car can learn to drive autonomously in the game environment and that the performance of the RL algorithm improves with appropriate reward structures and hyperparameters. The outcomes of this study’s conclusions may have an impact on the creation of autonomous driving systems and robotics.