The modern needs of mobile robots in a professional environment require them to navigate the same spaces as humans. It is crucial to be able to perform their tasks without interrupting people or being interrupted by human activity. Fundamentally, mobile robots should be able to adjust their trajectory when encountering humans and still manage to reach their goal. In an effort to address this need, the system proposed in this work combines a global navigation planning with a deep reinforcement learning (DRL) algorithm, specifically Twin Delayed Deep Deterministic Policy Gradient (TD3), to adapt in the presence of moving individuals. The methods were tested in a simulated environment where human activity varied (stationary humans vs. moving humans in the scene) to mimic the context of a super market aisle. The preliminary results of the simulation show that the hybrid algorithm succeeded in reaching its goal and its behavior is not affected between static and dynamic environments. This contrasts the performance of either method in isolation which changes drastically between encountering stationary and moving humans. Further development and integration with a physical system is scheduled to validate the simulation results and further refine the combination of the two algorithms.