Hybrid Reconfigurable Intelligent Surface (HRIS)assisted localization is pivotal for several sixth-generation wireless applications. HRIS comprises active and passive reflecting elements (REs) to support multiple users and targets in the near-field (NF) localization framework. In the HRIS system, active REs are connected to the power amplifiers (PAs) and phase shifters, while passive REs are only connected to the phase shifters. Optimizing the number of connections between active REs and PAs can significantly improve localization. This work proposes a novel Q learning-based active RE-PA association method to improve NF localization. In particular, the power allocation between each PA-Active RE and the number of connections between PA and active RE are optimized to reduce the system's power consumption. This reduces the hardware complexity due to the connection of each active RE with a separate PA. The experimental results have confirmed that the proposed algorithm outperforms the existing schemes and effectively compensates for the performance degradation caused by the reduction in PAs.