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Cloud radio access network (C-RAN) is a promising architecture to fulfill the ever-increasing resource demand in telecommunication networks. In C-RAN, a base station is decoupled into baseband unit (BBU) and remote radio head (RRH). The BBUs are further centralized and virtualized as virtual machines (VMs) inside a BBU pool. This architecture can meet the massively increasing cellular data traffic demand. However, resource management in C-RAN needs to be designed carefully in order to reach the objectives of energy saving and to meet the user demand over a long operational period. Since the user demands are highly dynamic in different times and locations, it is challenging to perform the optimal resource management. In this paper, we exploit a deep reinforcement learning (DRL) model to learn the spatial and temporal user demand in C-RAN, and propose an algorithm that resizes the VMs to allocate …