Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers

Abstract

The increasing popularity of cruise tourism has led to the need for effective planning and management strategies to enhance the city tour experience for cruise passengers. This paper presents a deep reinforcement learning (DRL)-based planner specifically designed to optimize city tours for cruise passengers. By leveraging the power of DRL, the proposed planner aims to maximize the number of visited attractions while considering constraints such as time availability, attraction capacities, and travel distances. The planner offers an intelligent and personalized approach to city tour planning, enhancing the overall satisfaction of cruise passengers and minimizing the negative impacts on the city’s infrastructure. An experimental evaluation was conducted considering Naples’s fourteen most attractive points of interest. Results show that, with 30 state variables and more than 19∗1012 possible states to be explored, the DRL-based planner converges to an optimal solution after only 20,000 learning steps.

Keywords

• intelligent transportation systems
• smart cities
• deep reinforcement learning
• optimal planning