This study proposes a reinforcement learning-based collaborative operation model between hotel service robots and human employees to address the dynamic task allocation problem in hotel service environments. Hotel service operations involve various customer requests that occur unpredictably over time, while the availability of robots and employees continuously changes according to operational conditions. Consequently, conventional static optimization approaches have limitations in effectively reflecting the uncertainty and real-time characteristics of actual hotel operation. To represent a realistic service environment, a virtual hotel consisting of 15 floors and 117 guest rooms was constructed. The operational environment included delivery and retrieval tasks that could be performed by service robots, as well as complaint-handling tasks that were exclusively assigned to human employees. The hotel service process was formulated as a Markov Decision Process (MDP), and a Proximal Policy Optimization (PPO) algorithm was employed to learn adaptive task allocation policies for service robots. In addition, a hybrid collaboration framework was developed in which reinforcement learning-based robots operated alongside rule-based human employees. While the PPO agents dynamically selected tasks according to the current system state, human employees followed predefined rule-based dispatching policies, enabling the evaluation of collaborative decision-making between heterogeneous service agents. Numerical experiments were conducted under static, dynamic, and dynamic collaboration scenarios, and the proposed model was compared with a CPLEX-based optimization benchmark. The results demonstrate that the proposed approach effectively adapts to real-time task arrivals and changing operational conditions. Furthermore, the hybrid collaboration structure improved overall operational performance by combining the adaptability of reinforcement learning with the stability and predictability of rule-based decision-making. The findings suggest that reinforcement learning can serve as an effective decision-making framework for dynamic task allocation in hotel service operations and that collaboration between learning-based robots and rule-based human workers can further enhance operational efficiency in complex service environments.