Path following is a critical capability for robots, ensuring precise navigation and task execution in various applications such as autonomous driving, delivery services, and industrial automation. Traditional methods often struggle in dynamic environments characterized by unpredictability and change. Reinforcement Learning (RL) has proven to be an effective method in overcoming these challenges, offering robust and adaptable solutions by generalizing learning from diverse experiences. However, the extensive training times associated with RL algorithms pose significant barriers to their practical deployment. This research paper proposes a novel hybrid adaptive exploration approach to drastically reduce training durations for visual path-following robots. By tailoring exploration mechanisms to prioritize informative experiences, we demonstrate a method to enhance learning efficiency. Our proposed algorithm showed a 67% improvement in simulation performance and was twice as effective in real-world environments compared to existing baselines. We conducted experiments in both virtual environments and with real robots in real-world settings, providing robust evidence to support our approach. Our findings pave the way for more agile and responsive robotic systems in dynamic environments.