Reinforcement learning is designed to maximize rewards through trial-and-error learning. Unlike traditional machine learning methods, it does not provide explicit instructions for each action (Sutton & Barto, 2015). The agent observes the current state, selects actions based on exploration and exploitation strategies (Terven, 2025), and receives a reward (rt+1), updating its knowledge of the environment for future decisions. Q-learning, a model-free reinforcement learning technique, divides operations into discrete episodes, updating Q-values based on actions taken and rewards received (Premakumari et al., 2025; Alavizadeh et al., 2022)). This approach allows the system to learn optimal policies, balancing short-term and long-term rewards.

An online booking system replaces manual spreadsheets and data entry, automating processes such as payment, scheduling, availability tracking, and notifications. It enables 24/7 bookings, accommodating customer preferences such as partial or full payments. Automated features also support reporting and resource management, benefiting both customers and resort staff. Hence, this system ensures that customers can make bookings at any time without being constrained by operating hours. It enhances accessibility, convenience, and overall customer satisfaction. Additionally, the system safeguards personal information through a dedicated data privacy interface, preventing fraud, identity theft, and other security risks associated with online reservations.