A Real-World-Oriented Multi-Task Allocation Approach Based on Multi-Agent Reinforcement Learning in Mobile Crowd Sensing

Han, Ji-Sheng; Zhang, Zhenyu; Wu, Xiaohong

doi:10.3390/info11020101

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…e central node is also responsible for task allocation and result recovery. Han et al [16] studied the MARL algorithm, combined with the idea of Q-learning, and optimized the task allocation time based on the heterogeneity of different participants and tasks under the premise of satisfying quality constraints. A real-world multitask allocation method based on multiagent reinforcement learning is proposed, and the optimization goal is defined as the pursuit of shorter task allocation time under the premise of satisfying quality constraints.…”

Section: Research On Task Allocation Mechanism Of Mobile Crowdmentioning

confidence: 99%

A Task Allocation Algorithm for Coal Mine Mobile Crowd Sensing Based on Weighted Undirected Graph

Wang

Chen

Jiang

et al. 2021

Security and Communication Networks

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Existing applications of mining intelligent mobile terminals are all for the sensing of individual terminals, without considering the group attributes of terminal carriers and the cooperation opportunities brought by terminal movement, which is unable to complete large-scale and complex sensing tasks. In this article, mobile crowd sensing and mine safety monitoring are combined to construct the mobile crowd sensing network for coal mine. Aiming at the task allocation problem, a task allocation mechanism based on a weighted undirected graph is proposed. First, considering the similarity of task completion location and time and quality of data collected, the reputation evaluation model of miners is designed. In order to optimize the reputation of miners participating in the task, an emergency task allocation algorithm based on the weighted undirected graph is proposed. Second, based on the fatigue degree of miners and the residual energy of intelligent mobile terminals, the miner status evaluation model is constructed, which is combined with the reputation model to design a nonemergency task allocation algorithm, aiming at further optimizing the task allocation results. The simulation results show that the proposed algorithm has better performance in allocation time for sensing task, task allocation success rate, average reputation, and status value of miners.

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Section: Research On Task Allocation Mechanism Of Mobile Crowdmentioning

confidence: 99%

A Task Allocation Algorithm for Coal Mine Mobile Crowd Sensing Based on Weighted Undirected Graph

Wang

Chen

Jiang

et al. 2021

Security and Communication Networks

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“…BSDA adopts three important mechanisms to prevent privacy leakage and develops a deep reinforcement learning method for energy-efficient data aggregation. Han et al [37] proposed a real-world-oriented multitask assignment method based on multiagent reinforcement learning. This method fully considers worker and task heterogeneity.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement Learning

Shi

et al. 2023

Wireless Communications and Mobile Computing

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Task assignment is a key issue in mobile crowdsensing (MCS). Previous task assignment methods were mainly static offline assignment. However, the MCS platform needs to process dynamically changing workers and tasks online in the actual assignment process. Hence, a reliable dynamic assignment strategy is crucial to improving the platform’s efficiency. This paper proposes an MCS dynamic task assignment framework to solve the task maximization assignment problem with spatiotemporal properties. First, a single worker is modeled for the Markov decision process, and a deep reinforcement learning algorithm (DDQN) is used to perform offline learning on historical task data. Then, in the dynamic assignment process, we consider the impact of current decisions on future decisions. Use the maximum flow model to maximize the number of tasks completed in each period while maximizing the expected Q value of all workers to achieve the optimal global assignment. Experiments show that the strategy proposed in this paper has good performance compared with the baseline strategy under different conditions.

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“…The work [10] develops a game theory-based method for simple agent-task allocation problems. The work [11] proposes a real-world multi-task allocation approach for mobile crowd sensing based on multi-agent reinforcement learning, which considers heterogeneity and enables independent learning to optimize task quality. These works give some theoretical analysis on multi-agent multi-task problems, and develops some methods for simple applications.…”

Section: Multiple Agents For Multiple Tasksmentioning

confidence: 99%

A hierarchical multi-agent allocation-action learning framework for multi-subtask games

Li,

Zhang

et al. 2023

Complex Intell. Syst.

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Great progress has been made in the domain of multi-agent reinforcement learning in recent years. Most work concentrates on solving a single task by learning the cooperative behaviors of agents. However, many real-world problems are normally composed of a set of subtasks in which the execution order follows a certain procedure. Cooperative behaviors should be learned on the premise that agents are first allocated to those subtasks. In this paper, we propose a hierarchical framework for learning the dynamic allocation of agents among subtasks, as well as cooperative behaviors. We design networks corresponding to agents and subnetworks, respectively, which constitute the whole hierarchical network. For the upper layer, a novel allocation learning mechanism is devised to map an agent network to a subtask network. Each agent network could only be assigned to only one subtask network at each time step. For the lower layer, an action learning module is designed to compute appropriate actions for each agent with the allocation result. The agent networks together with the subtask networks are updated by a common reward obtained from the environment. To evaluate the effectiveness of our framework, we conduct experiments in two challenging environments, i.e., Google Research Football and SAVETHECITY. Empirical results show that our framework achieves much better performance than other recent methods.

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A Real-World-Oriented Multi-Task Allocation Approach Based on Multi-Agent Reinforcement Learning in Mobile Crowd Sensing

Cited by 10 publications

References 15 publications

A Task Allocation Algorithm for Coal Mine Mobile Crowd Sensing Based on Weighted Undirected Graph

A Task Allocation Algorithm for Coal Mine Mobile Crowd Sensing Based on Weighted Undirected Graph

Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement Learning

A hierarchical multi-agent allocation-action learning framework for multi-subtask games

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