Deep Learning for Reinforcement Learning
Enhancing Autonomous System Decision-Making
Keywords:
Reinforcement Learning, Autonomous Systems, Robotics, Drones, Intelligent SystemsAbstract
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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