A Survey on Deep Reinforcement Learning Applications in Autonomous Systems

Abstract

Deep Reinforcement Learning (DRL) is a rapidly evolving field that has significantly influenced autonomous systems such as self-driving cars, drones, and robotics. This survey aims to provide a comprehensive overview of the state-of-the-art DRL techniques and their applications in real-world autonomous systems. The paper discusses various architectures such as Deep Q-Networks (DQNs), Policy Gradient methods, and Actor-Critic models, analyzing their strengths and weaknesses in dynamic and complex environments. Moreover, the study focuses on how DRL can address specific challenges in decision-making, navigation, and obstacle avoidance for autonomous vehicles. The integration of DRL with sensor data, such as LIDAR and camera inputs, is explored to understand how these systems can learn more efficiently in real-time environments. Furthermore, the paper examines the scalability of DRL models in large-scale autonomous systems and presents the most recent advancements in this domain. Challenges such as overfitting, reward shaping, and sample inefficiency are discussed, alongside potential future directions like multi-agent systems and cooperative DRL. The review also highlights real-world applications and case studies, illustrating how DRL is implemented in autonomous systems. Finally, the ethical and safety concerns associated with autonomous DRL systems are considered, particularly in the context of self-driving cars and other autonomous technologies that interact with humans.