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 which consist of a sequence of ordered Virtual Network Functions (VNFs). To achieve efficient edge resources allocation for SFC requests and optimal profit for edge service providers%2C we formulate the SFC placement problem in an edge environment and propose a multi-agent Reinforcement Learning (RL) scheme to address the problem. The proposed scheme employs a set of RL agents to collaboratively make SFC placement decisions%2C such as path selection%2C VNF configuration%2C and VNF deployment. Simulation results show our model can improve the profit of edge service providers by 12%25 compared with a heuristic solution.?quality=80&w=800)
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