Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning

The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algorithms are commonly applied to optimize ITS applic...

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Veröffentlicht in:	arXiv.org 2024-03
Hauptverfasser:	Tan, Jing, Khalili, Ramin, Karl, Holger
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Cloud computing Computer Science - Artificial Intelligence Computer Science - Learning Computer Science - Multiagent Systems Edge computing Intelligent transportation systems Machine learning Mathematics - Optimization and Control Multiagent systems Multiple objective analysis Resource management
Online-Zugang:	Volltext
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