Handover management is an important issue in mobile 5G networks. A controller must decide when each piece of user equipment (UE) should switch its serving base station to maximize throughput while minimizing handovers, ping-pong switching, and connection failures. This paper presents a reproducible simulation environment and a systematic review of handover strategies in a multi-user scenario with four base stations and ten mobile UEs, using a 3GPP Urban Macro channel model with log-normal shadowing. We evaluate four controllers over 100 independent episodes: the 3GPP A3-event algorithm with hysteresis and time-to-trigger, an ideal Strongest-Cell rule, a No-Handover lower bound, and a learned controller trained with Proximal Policy Optimization (PPO). The results show that the standardized A3-event policy provides the best overall balance. It achieves the highest mean system throughput at 89.1 Mbit/s, the fewest handovers per episode at 21.7, and the lowest ping-pong rate at 0.098 among all methods assessed. The Strongest-Cell rule offers similar throughput but results in about 2.6 times more handovers. The learned controller shows stable training but does not reach the performance of the tuned A3 baseline based on the current reward and observation design. We discuss why the hysteresis-and-TTT logic remains a strong baseline and outline the conditions where a learned policy could outperform it. The entire environment and evaluation process are available for others to reproduce.