Teaching an AI agent how to drive on complex racetracks using reinforcement learning

Abstract

The main objective of this thesis is to educate a reinforcement learning (RL) agent on acquiring the skill of driving a car in the Unity environment. The central focus of this thesis is to train a reinforcement learning (RL) agent utilizing a deep neural network to effectively control and optimize its behavior in order to achieve a specific task or environment. This will be achieved through the application of various reward functions, utilizing the Proximal Policy Optimization (PPO) algorithm. Throughout the training process, I conducted extensive experimentation with various combinations of hyperparameters and network settings. Additionally, I explored different approaches for calculating rewards in order to identify the most effective method for achieving optimal RL agent performance.