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

arXiv: https://arxiv.org/abs/2508.20688 · HTML: https://mlsanity.com/paper/2508.20688/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles](https://mlsanity.com/paper/2310.18891/) (2023)
- [Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery](https://mlsanity.com/paper/1902.05183/) (2019)
- [A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting](https://mlsanity.com/paper/1901.03327/) (2019)
- [Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents](https://mlsanity.com/paper/2505.11708/) (2025)
- [Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition](https://mlsanity.com/paper/2011.03603/) (2020)
- [Statistically Distinct Plans for Multi-Objective Task Assignment](https://mlsanity.com/paper/2312.07292/) (2023)

## 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.