  Radio access network (RAN) slicing is a key technology that enables 5G
network to support heterogeneous requirements of generic services, namely
ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband
(eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to
optimize the performance of URLLC and eMBB services. In a large time-scale, an
SDN controller allocates radio resources to gNodeBs according to the
requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB
allocates its available resources to its end-users and requests, if needed,
additional resources from adjacent gNodeBs. We formulate this problem as a
non-linear binary program and prove its NP-hardness. Next, for each time-scale,
we model the problem as a Markov decision process (MDP), where the large-time
scale is modeled as a single agent MDP whereas the shorter time-scale is
modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for
exploration and exploitation (EXP3) to solve the single-agent MDP of the large
time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the
multi-agent MDP of the short time-scale resource allocation. Extensive
simulations show that our approach is efficient under different network
parameters configuration and it outperforms recent benchmark solutions.
