article · Computer Science Bulletin · 2023

REINFORCEMENT LEARNING: APPLICATIONS IN ROBOTICS AND AUTONOMOUS SYSTEMS

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Abstract

Reinforcement learning (RL) has emerged as a powerful machine learning technique for enabling intelligent agents to make decisions through interactions with their environment. This paper explores the applications of RL in robotics and autonomous systems, providing insights into how RL algorithms enable robots and autonomous systems to perform tasks autonomously in dynamic and uncertain environments. The study covers key advancements, challenges, and future trends in the integration of RL within autonomous systems, highlighting the potential to revolutionize industries such as manufacturing, healthcare, transportation, and security. Through a review of various RL models and case studies, this paper demonstrates the applicability of RL techniques to real-world problems, emphasizing their practical applications and performance in robotic systems. Finally, the paper outlines potential research directions and the evolution of RL in intelligent robotic and autonomous systems.

Cite this paper

APA 7

Khan, M. A. (2023). REINFORCEMENT LEARNING: APPLICATIONS IN ROBOTICS AND AUTONOMOUS SYSTEMS. Computer Science Bulletin, 6(01), 67–74. https://doi.org/10.71465/csb106

MLA 9

Khan, Muhammad Ali. “REINFORCEMENT LEARNING: APPLICATIONS IN ROBOTICS AND AUTONOMOUS SYSTEMS.” Computer Science Bulletin, vol. 6, no. 01, 2023, pp. 67–74, https://doi.org/10.71465/csb106.

Chicago

Khan, Muhammad Ali. 2023. “REINFORCEMENT LEARNING: APPLICATIONS IN ROBOTICS AND AUTONOMOUS SYSTEMS.” Computer Science Bulletin 6 (01): 67–74. https://doi.org/10.71465/csb106.

Harvard

Khan, M.A. (2023) ‘REINFORCEMENT LEARNING: APPLICATIONS IN ROBOTICS AND AUTONOMOUS SYSTEMS’, Computer Science Bulletin, 6(01), pp. 67–74. Available at: https://doi.org/10.71465/csb106.

BibTeX

@article{khan2023reinforcement,
  title = {{REINFORCEMENT LEARNING: APPLICATIONS IN ROBOTICS AND AUTONOMOUS SYSTEMS}},
  author = {Khan, Muhammad Ali},
  journal = {Computer Science Bulletin},
  volume = {6},
  number = {01},
  pages = {67--74},
  year = {2023},
  doi = {10.71465/csb106}
}

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