Online service migration decision in vehicle edge computing under partial observation
For complex vehicle edge computing scenarios with multiple users and global information scarcity,a vehicle edge computing service migration strategy based on multi-agent reinforcement learning was proposed.This allowed vehicles to learn incomplete system information and make distributed online migration decisions,even with only partial observations.This strateged employed a multi-agent deep deterministic policy gradient algorithm improved by Gumbel-Softmax sampling.Through collaboration and competition among users,a common goal was achieved,making the system more flexible and improving the overall benefit and stability of the system.Simulation results from real data sets show that this strategy converges quickly,performs well in scenarios with different numbers of users and task arrival rates,and exhibits superior robustness and stability compared to other comparative strategies.
vehicule edge computingmulti-agent reinforcement learningonline service migration decision-making
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