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This paper investigates algorithms for controlling and coordinating autonomous machines in complex environments. We focus on task allocation methods using computational intelligence (CI) and deep reinforcement learning (RL), analyzing the strengths and weaknesses of the investigated methods. We also suggest and discuss various future research directions for improving existing algorithms or developing new methods to enhance the employability and performance of autonomous machines in practical applications. Recent advances in deep RL have significantly contributed to the literature on the control and coordination of autonomous machines, demonstrating a growing trend in this field.
Takeaways, Limitations
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Takeaways:
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We propose that computational intelligence (CI) and deep reinforcement learning (RL) are viable approaches for solving complex task assignment problems in dynamic and uncertain environments.
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Recent advances in deep reinforcement learning are driving growth in the field of autonomous machine control and coordination.
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Provides a comprehensive overview of progress in machine learning research related to autonomous machines.
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It highlights unexplored areas, presents new methodologies, and suggests new directions for future research.
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Limitations:
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Details on actual implementations and performance evaluations of specific algorithms are limited.
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No specific research plan or methodology is presented for the future research direction suggested in the paper.
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There may be a lack of discussion about generalizability to different types of autonomous machines and work environments.
