  We present a Reinforcement Learning (RL) approach to the problem of
controlling the Discontinuous Reception (DRX) policy from a Base Transceiver
Station (BTS) in a cellular network. We do so by means of optimally timing the
transmission of fast Layer-2 signaling messages (a.k.a. Medium Access Layer
(MAC) Control Elements (CEs) as specified in 5G New Radio). Unlike more
conventional approaches to DRX optimization, which rely on fine-tuning the
values of DRX timers, we assess the gains that can be obtained solely by means
of this MAC CE signalling. For the simulation part, we concentrate on traffic
types typically encountered in Extended Reality (XR) applications, where the
need for battery drain minimization and overheating mitigation are particularly
pressing. Both 3GPP 5G New Radio (5G NR) compliant and non-compliant ("beyond
5G") MAC CEs are considered. Our simulation results show that our proposed
technique strikes an improved trade-off between latency and energy savings as
compared to conventional timer-based approaches that are characteristic of most
current implementations. Specifically, our RL-based policy can nearly halve the
active time for a single User Equipment (UE) with respect to a na\"ive MAC CE
transmission policy, and still achieve near 20% active time reduction for 9
simultaneously served UEs.
