Deep Reinforcement Learning for Sequential Decision Making: Investigating deep reinforcement learning algorithms for sequential decision-making tasks in AI

Abstract

Deep reinforcement learning (DRL) has emerged as a powerful approach for solving sequential decision-making problems in artificial intelligence (AI). This paper provides an overview of DRL algorithms and their applications in various domains. We discuss key concepts, such as the Markov decision process (MDP) framework, value functions, policy gradients, and explore how DRL can be used to tackle complex sequential decision-making tasks. Additionally, we review recent advances and challenges in DRL research, including sample efficiency, exploration-exploitation trade-offs, and generalization to new environments. Finally, we discuss potential future directions for DRL research, highlighting the importance of addressing these challenges to further advance the field.