Deep Reinforcement Learning for Autonomous Systems: Challenges and Opportunities

Abstract

Deep Reinforcement Learning (DRL) has recently emerged as a powerful approach for building autonomous systems that can learn to perform complex tasks without explicit instructions. DRL combines deep learning with reinforcement learning, enabling systems to learn from their experiences through trial and error. In this paper, we explore the challenges and opportunities of using DRL in autonomous systems. We review recent advances in DRL research, including the use of actor-critic methods, deep Q-networks, and policy gradient methods. We discuss the challenges of training DRL models for autonomous systems, such as the high dimensionality of state and action spaces and the need for extensive training data. We also examine the ethical and safety implications of using DRL in autonomous systems and provide recommendations for future research in this area. Finally, we highlight some promising opportunities for the application of DRL in autonomous systems, including robotics, transportation, and healthcare.