Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning
Nguyen, Thanh Thi Nguyen, Quoc Viet Hung Kua, Jonathan Razzak, Imran Nguyen, Dung Nahavandi, Saeid
Nguyen, Thanh Thi
Nguyen, Quoc Viet Hung
Kua, Jonathan
Razzak, Imran
Nguyen, Dung
Nahavandi, Saeid
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Computational Biology
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Conference proceeding
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Abstract
Enabling multiple autonomous machines to perform reliably requires the development of efficient cooperative control algorithms. This paper presents a survey of algorithms that have been developed for controlling and coordinating autonomous machines in complex environments. We especially focus on task allocation methods using computational intelligence (CI) and deep reinforcement learning (RL). The advantages and disadvantages of the surveyed methods are analysed thoroughly. We also propose and discuss in detail various future research directions that shed light on how to improve existing algorithms or create new methods to enhance the employability and performance of autonomous machines in real-world applications. The findings indicate that CI and deep RL methods provide viable approaches to addressing complex task allocation problems in dynamic and uncertain environments. The recent development of deep RL has greatly contributed to the literature on controlling and coordinating autonomous machines, and it has become a growing trend in this area. It is envisaged that this paper will provide researchers and engineers with a comprehensive overview of progress in machine learning research related to autonomous machines. It also highlights underexplored areas, identifies emerging methodologies, and suggests new avenues for exploration in future research within this domain.
Citation
T.T. Nguyen, Q.V.H. Nguyen, J. Kua, I. Razzak, D. Nguyen, S. Nahavandi, "Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning," 2026, pp. 1244-1251.
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2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4611 Machine Learning
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Source
2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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IEEE