mlsanityData to 14 Oct 2026

Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning

arXiv 2508.20688 ↗First posted 28 Aug 2025cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 75 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

11 of 75 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2016Learning Multiagent Communication with Backpropagationcited by 366
  5. 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 127
  6. 2023Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehiclescited by 13
  7. 2019Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgerycited by 12
  8. 2019A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cuttingcited by 9
  9. 2025Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agentscited by 4
  10. 2020Fast Near-Optimal Heterogeneous Task Allocation via Flow Decompositioncited by 1
  11. 2023Statistically Distinct Plans for Multi-Objective Task Assignmentcited by 1

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  2. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
  3. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  4. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  5. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  6. 2024A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
  7. 2025Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
  8. 2025A Survey of Reinforcement Learning for Optimization in Automation