• Corpus ID: 231933789

Resource allocation in dynamic multiagent systems

@article{Creech2021ResourceAI,
  title={Resource allocation in dynamic multiagent systems},
  author={Niall Creech and Natalia Criado and Simon Miles},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.08317}
}
Resource allocation and task prioritisation are key problem domains in the fields of autonomous vehicles, networking, and cloud computing. The challenge in developing efficient and robust algorithms comes from the dynamic nature of these systems, with many components communicating and interacting in complex ways. The multi-group resource allocation optimisation (MG-RAO) algorithm we present uses multiple function approximations of resource demand over time, alongside reinforcement learning… 
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References

SHOWING 1-10 OF 39 REFERENCES

Issues in Multiagent Resource Allocation

A survey of some of the most salient issues in Multiagent Resource Allocation, including various languages to represent the pref-erences of agents over alternative allocations of resources as well as different measures of social welfare to assess the overall quality of an allocation.

Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks

This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards and proposes an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning.

Reinforcement Learning in Dynamic Task Scheduling: A Review

The paper addresses the results of the study by means of the state-of-the-art on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.

A Comprehensive Survey of Multiagent Reinforcement Learning

The benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied, and an outlook for the field is provided.

Adaptive Task and Resource Allocation in Multi-Agent Systems

An adaptive organizational poli y for multi-agent systems that uses ideas from omputational market systems to allo ate resour es (in the form of problem solving agents) to organizations to minimize the number of lost requests by the resour e allo ation proto ol.

A Survey on Multi-Agent Reinforcement Learning Methods for Vehicular Networks

A survey on research issues related to vehicular networks such as resource allocation, data offloading, cache placement, ultra-reliable low latency communication (URLLC), and high mobility and the potential applications of MARL that enables decentralized and scalable decision making in vehicle-to-everything (V2X) scenarios are provided.

Resource allocation under uncertainty using the maximum entropy principle

This paper considers a number of data sources with uncertain bit rates, sharing a set of parallel channels with time-varying and possibly uncertain transmission capacities, and presents a method for allocating the channels so as to maximize the expected system throughput.

Multiagent resource allocation

The first meeting of the Technical Forum Group on Multiagent Resource Allocation (TFG-MARA) is reported on, which was held as part of the Second AgentLink III Technical Forum in Ljubljana.

Multi Agent Resource Allocation: a Comparison of Five Negotiation Protocols

A deep comparison is drawn according to different criteria that involve general features of the systems; adherence to widely accepted agent definitions; domain, purpose, and approach; analysis, design and implementation of the negotiation protocol.

Reinforcement learning-based multi-agent system for network traffic signal control

Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings.