Reinforcement Learning for Autonomous Systems

Reinforcement Learning (RL) has emerged as a vital component in the development of autonomous systems. However, several challenges, such as high computational demands, limited generalization in dynamic environments, and the need for extensive training data, hinder its effectiveness. This paper reviews recent advancements in RL techniques for UAVs and AVs, focusing on methods like Double DQN, Actor-Critic, and self-supervised learning to address these challenges. The objective is to analyze decision-making processes, reliability, and adaptability of RL models in real-world applications.