  5G is regarded as a revolutionary mobile network, which is expected to
satisfy a vast number of novel services, ranging from remote health care to
smart cities. However, heterogeneous Quality of Service (QoS) requirements of
different services and limited spectrum make the radio resource allocation a
challenging problem in 5G. In this paper, we propose a multi-agent
reinforcement learning (MARL) method for radio resource slicing in 5G. We model
each slice as an intelligent agent that competes for limited radio resources,
and the correlated Q-learning is applied for inter-slice resource block (RB)
allocation. The proposed correlated Q-learning based interslice RB allocation
(COQRA) scheme is compared with Nash Q-learning (NQL),
Latency-Reliability-Throughput Q-learning (LRTQ) methods, and the priority
proportional fairness (PPF) algorithm. Our simulation results show that the
proposed COQRA achieves 32.4% lower latency and 6.3% higher throughput when
compared with LRTQ, and 5.8% lower latency and 5.9% higher throughput than NQL.
Significantly higher throughput and lower packet drop rate (PDR) is observed in
comparison to PPF.
