Intervention-Assisted Policy Gradient Methods for Online Stochastic Queuing Network Optimization: Technical Report

arXiv:2404.04106v1 Announce Type: cross
Abstract: Deep Reinforcement Learning (DRL) offers a powerful approach to training neural network control policies for stochastic queuing networks (SQN). However, traditional DRL methods rely on offline simulations or static datasets, limiting their real-world application in SQN control. This work proposes Online Deep Reinforcement Learning-based Controls (ODRLC) as an alternative, where an intelligent agent interacts directly with a real environment and learns an optimal control policy from these online interactions. SQNs present a challenge for ODRLC …

abstract application arxiv control cs.ai cs.lg datasets gradient however network networks neural network offline optimization policies policy reinforcement reinforcement learning report simulations stochastic technical training type work world