Deep Reinforcement Learning Unleashing the Power of AI in Decision-Making

Jeff Shuford ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 1, issue 1

Abstract: Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm in the field of artificial intelligence (AI), offering unprecedented capabilities in decision-making across diverse domains. This article explores the profound impact of DRL on enhancing the decision-making capabilities of AI systems, elucidating its underlying principles, applications, and implications.DRL represents a fusion of deep learning and reinforcement learning, enabling machines to learn complex behaviors and make decisions by interacting with their environment. The utilization of neural networks allows DRL algorithms to handle high-dimensional input spaces, making it well-suited for tasks that involve intricate decision-making processes.One of the key strengths of DRL lies in its ability to address problems with sparse and delayed rewards, common challenges in traditional reinforcement learning. Through a process of trial and error, DRL algorithms can learn optimal decision strategies by navigating through a vast decision space, adapting to dynamic environments, and maximizing cumulative rewards over time.The applications of DRL span various domains, including robotics, finance, healthcare, gaming, and autonomous systems. In robotics, DRL facilitates the development of intelligent agents capable of autonomously navigating complex environments, performing intricate tasks, and adapting to unforeseen circumstances. In finance, DRL is leveraged for portfolio optimization, algorithmic trading, and risk management, demonstrating its potential to revolutionize traditional financial strategies.

Keywords: Deep Reinforcement Learning; artificial intelligence (AI); Decision-Making (search for similar items in EconPapers)
Date: 2024
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Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek

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