Network Function Virtualization (NFV) technology can tie together a set of Virtual Network Functions (VNFs) as a Service Function Chain (SFC). Although NFV is a promising technology for the placement of user service requests, SFCs often suffer from high delay and low throughput. This paper develops an efficient approach based on Deep Reinforcement Learning (DRL) with the aim of deploying VNFs with low delay during heterogeneous bandwidth demands in Data Center Networks (DCNs). We design an architecture that exploits the dependencies of VNFs to parallelize them and also preserve more resources for processing future requests by extracting the distribution of initialized VNFs. Our algorithm is expected to maximize the long-term reward and improve the computational acceleration in provisioning user service requests. Our evaluations show that the proposed algorithm reduces the service delay by 12% through parallelized VNFs.