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Quantitative Studies Of Deep Reinforcement Learning In Gaming, Robotics And Real-World Control Systems

Author

Listed:
  • MUHAMMAD UMAR KHAN

    (Assistant Professor, Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan)

  • SOMIA MEHAK

    (Department of Computer Science, NUML Multan Campus, Pakistan)

  • DR. WAJIHA YASIR

    (Assistant Professor, COMSATS University Islamabad, Abbott bad Campus, Pakistan)

  • SHAGUFTA ANWAR

    (Department of Computer Science and Technology, Lahore LEADs University Lahore, SSE CS GGHS BHILOMAHAR ,Daska ,Sialkot, Pakistan)

  • MUHAMMAD USMAN MAJEED

    (Faculty of Computing and Information Technology, University of the Punjab Lahore, Pakistan)

  • HAFIZ ARSLAN RAMZAN

    (Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan)

Abstract

Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm with profound implications for gaming, robotics, real-world control systems, and beyond. This quantitative analysis delves into the applications of DRL across these domains, assessing its capabilities, challenges, and potential. In the gaming realm, we showcase DRL's prowess through significant score improvements in benchmark games, with DQN and PPO leading the way. A3C underscores its adaptability through strong generalization within the gaming domain. While specific robotics and real-world control results are not presented here, their promise in enhancing task completion and precision is evident. Sample efficiency and safety strategies address critical concerns, demonstrating DRL's capacity to optimize resource utilization and ensure robustness. Generalization and transfer learning underscore DRL's adaptability to new scenarios. While these findings are not empirical but illustrative, they emphasize DRL's versatility and highlight the need for continued research to unlock its full potential in addressing complex real-world challenges.

Suggested Citation

  • Muhammad Umar Khan & Somia Mehak & Dr. Wajiha Yasir & Shagufta Anwar & Muhammad Usman Majeed & Hafiz Arslan Ramzan, 2023. "Quantitative Studies Of Deep Reinforcement Learning In Gaming, Robotics And Real-World Control Systems," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 12(2), pages 389-395.
    • Handle: RePEc:rfh:bbejor:v:12:y:2023:i:2:p:389-395
    DOI: https://doi.org/10.61506/01.00019
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    References listed on IDEAS

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    1. Muhammad Umar Khan & Somia Mehak & Dr. Wajiha Yasir & Shagufta Anwar & Muhammad Usman Majeed & Hafiz Arslan Ramzan, 2023. "Quantitative Studies Of Deep Reinforcement Learning In Gaming, Robotics And Real-World Control Systems," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 12(2), pages 389-395.
    2. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    3. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
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    Cited by:

    1. Muhammad Umar Khan & Somia Mehak & Dr. Wajiha Yasir & Shagufta Anwar & Muhammad Usman Majeed & Hafiz Arslan Ramzan, 2023. "Quantitative Studies Of Deep Reinforcement Learning In Gaming, Robotics And Real-World Control Systems," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 12(2), pages 389-395.

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