  Virtualized Radio Access Network (vRAN) is one of the key enablers of future
wireless networks as it brings the agility to the radio access network (RAN)
architecture and offers degrees of design freedom. Yet, it also creates a
challenging problem on how to design the functional split configuration. In
this paper, a deep reinforcement learning approach is proposed to optimize
function splitting in vRAN. A learning paradigm is developed that optimizes the
location of functions in the RAN. These functions can be placed either at a
central/cloud unit (CU) or a distributed unit (DU). This problem is formulated
as constrained neural combinatorial reinforcement learning to minimize the
total network cost. In this solution, a policy gradient method with Lagrangian
relaxation is applied that uses a stacked long short-term memory (LSTM) neural
network architecture to approximate the policy. Then, a sampling technique with
a temperature hyperparameter is applied for the inference process. The results
show that our proposed solution can learn the optimal function split decision
and solve the problem with a $0.4\%$ optimality gap. Moreover, our method can
reduce the cost by up to $320\%$ compared to a distributed-RAN (D-RAN). We also
conclude that altering the traffic load and routing cost does not significantly
degrade the optimality performance.
