Existing robotic water jet control methods are limited by fixed spray configurations and low adaptability to complex or dynamic environments. These constraints hinder precise targeting in three-dimensional spaces. To overcome this, we propose a reinforcement learning-based water jet control framework that achieves accurate targeting without pose or angle restrictions. Specifically, we introduce Goal-Priority Hindsight Experience Replay (GPHER), a replay strategy that integrates the principles of Hindsight Experience Replay (HER), Prioritized Experience Replay (PER), and curriculum learning. GPHER dynamically adjusts sampling priorities based on goal-space distance, guiding training from simple to complex goals. Combined with Truncated Quantile Critics (TQCs), this approach accelerates convergence and enhances success rates. Both simulation and real-world experiments validate the robustness and adaptability of the proposed method, demonstrating its effectiveness for real-time robotic fluid control.