Deep Reinforcement Learning - Advances and Applications: Analyzing recent advances and applications of deep reinforcement learning techniques for solving complex decision-making tasks

Abstract

Deep reinforcement learning (DRL) has emerged as a powerful paradigm for solving complex decision-making tasks in various domains. This paper provides a comprehensive analysis of recent advances and applications of DRL techniques. We first review the fundamental concepts of reinforcement learning (RL) and deep learning, highlighting the key differences and challenges in combining them. Next, we discuss recent advancements in DRL algorithms, including deep Q-networks (DQN), policy gradient methods, and actor-critic architectures. We then examine the applications of DRL in areas such as robotics, gaming, finance, and healthcare, showcasing the effectiveness of DRL in solving real-world problems. Finally, we discuss challenges and future directions in DRL research, emphasizing the need for improved sample efficiency, generalization, and interpretability. This paper aims to provide researchers and practitioners with a comprehensive overview of the current state of DRL and its potential future developments.