  Several Radio Resource Management (RRM) use cases can be framed as sequential
decision planning problems, where an agent (the base station, typically) makes
decisions that influence the network utility and state. While Reinforcement
Learning (RL) in its general form can address this scenario, it is known to be
sample inefficient. Following the principle of Occam's razor, we argue that the
choice of the solution technique for RRM should be guided by questions such as,
"Is it a short or long-term planning problem?", "Is the underlying model known
or does it need to be learned?", "Can we solve the problem analytically?" or
"Is an expert-designed policy available?". A wide range of techniques exists to
address these questions, including static and stochastic optimization, bandits,
model predictive control (MPC) and, indeed, RL. We review some of these
techniques that have already been successfully applied to RRM, and we believe
that others, such as MPC, may present exciting research opportunities for the
future.
