DEEP REINFORCEMENT LEARNING: PRINCIPLES AND APPLICATIONS

Dr. Maria Bibi

doi:10.71465/csb89

journal article Jun 30, 2022

DEEP REINFORCEMENT LEARNING: PRINCIPLES AND APPLICATIONS

Dr. Maria Bibi

Computer Science Bulletin Vol. 5 No. 01 pp. 122-139 · International Study Counselor

View at Publisher Save 10.71465/csb89

Abstract

Deep Reinforcement Learning (DRL) combines the power of deep learning with reinforcement learning (RL) to solve complex decision-making tasks that involve sequential actions in dynamic environments. DRL has achieved significant breakthroughs in various domains, such as robotics, gaming, autonomous vehicles, and healthcare, due to its ability to learn optimal policies through interaction with the environment. This paper provides a comprehensive overview of the principles behind DRL, including the key concepts of reinforcement learning, deep neural networks, and their integration. We discuss the primary algorithms used in DRL, such as Deep Q-Networks (DQN), Policy Gradient methods, and Actor-Critic models. Additionally, we examine real-world applications where DRL has been successfully implemented, along with challenges related to exploration, stability, and scalability. The paper concludes by exploring future directions, including the potential for general AI and multi-agent systems.

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Published: Jun 30, 2022
Vol/Issue: 5(01)
Pages: 122-139

Authors

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Dr. Maria Bibi

Cite This Article

Dr. Maria Bibi (2022). DEEP REINFORCEMENT LEARNING: PRINCIPLES AND APPLICATIONS. Computer Science Bulletin, 5(01), 122-139. https://doi.org/10.71465/csb89

DEEP REINFORCEMENT LEARNING: PRINCIPLES AND APPLICATIONS

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