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

## TL;DR

This paper surveys the use of computational intelligence and deep reinforcement learning for task allocation in autonomous machines, analyzing current methods and proposing future research directions to improve their real-world applicability.

## Contribution

It provides a comprehensive overview of recent advances in CI and deep RL for autonomous machine coordination, highlighting strengths, weaknesses, and future research avenues.

## Key findings

- Deep RL has significantly advanced autonomous machine control.
- CI and deep RL are effective for complex, dynamic task allocation.
- The paper identifies underexplored areas and emerging methodologies.

## 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.