Deep Learning for Reinforcement Learning

Enhancing Autonomous System Decision-Making

Authors

  • Michael Green Green Senior Researcher, Department of Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA Author

Keywords:

Reinforcement Learning, Autonomous Systems, Robotics, Drones, Intelligent Systems

Abstract

The integration of deep learning with reinforcement learning (RL) represents a significant advancement in the field of autonomous systems, providing enhanced decision-making capabilities for applications such as robotics and drone navigation. Deep learning techniques, particularly deep neural networks, offer the ability to process and learn from large amounts of unstructured data, which can be effectively harnessed to improve the efficiency and accuracy of RL algorithms. This paper discusses the foundational principles of both deep learning and reinforcement learning, highlighting how deep learning architectures can be employed to optimize decision-making processes in autonomous systems. By examining various approaches that merge these two paradigms, this research delineates the benefits and challenges associated with their integration. Furthermore, real-world applications and case studies are presented to illustrate the impact of deep learning-enhanced RL on the performance of autonomous systems. The paper concludes with a discussion on future research directions and the potential for further advancements in this dynamic intersection of technologies.

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Published

27-12-2023

How to Cite

[1]
M. G. Green, “Deep Learning for Reinforcement Learning: Enhancing Autonomous System Decision-Making”, Distrib Learn Broad Appl Sci Res, vol. 9, pp. 400–407, Dec. 2023, Accessed: Nov. 23, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/150

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