A Brief Survey of Deep Reinforcement Learning Algorithms for Autonomous Systems

The advent of autonomous systems has propelled the integration of artificial intelligence (AI) and machine learning (ML) techniques, particularly deep reinforcement learning (DRL), to enhance decision-making capabilities. This research paper conducts an exhaustive survey of state-of-the-art DRL algorithms, focusing on their applicability and performance within the realm of autonomous systems. To find out how flexible and useful DRL algorithms are in real life, our research covers a lot of different areas, such as robots, self-driving cars, and unmanned flying vehicles. The study takes a close look at the most important parts of these algorithms, like neural network designs, exploration-exploitation strategies, and payment processes, to see how they affect how well independent systems work. Additionally, the study goes into detail about the problems and restrictions that come with using DRL in self-driving systems, covering everything from sample waste to safety concerns. Wealso look at newdevelopments andimprovements in DRLthatmightbeable to get around current problems and make way for future innovations in driverless technology. As a valuable resource for researchers, engineers, and practitioners working on the development and deployment of autonomous systems, this brief survey shows the pros, cons, and opportunities that come with different DRL algorithms in this ever-changing field.