# Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers +
## 2 Literature Analysis  
Most of the works in the literature focus on providing solutions to plan tours that maximize the tourist travel experience [7]. The tourist tour planning problem is difficult to address due to the diversity of involved objectives and preferences that cannot always be expressed as linear combinations, making classic optimization approaches unfeasible. These problems have been proven to be NP-hard [8], so heuristic techniques are adopted to solve them. Genetic algorithms are well-established heuristic approaches to solve such search and optimization problems. When considering the time to visit and user preferences over the points of interest, the itinerary planning can be formulated as a multi-objective optimization problem, as in [9] where an adaptive genetic algorithm is proposed. A genetic algorithm was also proposed in [10] to generate tourist itineraries that include not only touristic attractions but also restaurants, with the additional constraint that they should be visited at lunch or dinner time. In [11], the authors propose a novel automatic planning method based on a genetic algorithm to suggest multiple itineraries that satisfy the specific preferences of tourists while spending the least time on roads, visiting highly-rated PoIs, and visiting diverse PoIs. The proposed algorithm provides multiple optimized itineraries for the tourists so that they can choose their most preferred one. Solutions proposed for the team orienteering problems are adopted for generating itinerary plans as a path connecting a set of nodes with the constraint of not exceeding a budget score expressed in terms of time and cost, as in [12]. In [13], an algorithm is proposed to recommend personalized tours taking into account user preferences, the popularity of PoIs derived from geotagged photographs, and constraints on the duration and the starting and end point of the tour. The dispatch of cruise passengers arriving on the same day in a destination city to different city locations is addressed in [14], where a decision support system is proposed to organize their transportation from the port. The system computes the itinerary for the number of arriving passengers by taking into account their tourist needs and specific city events that may prevent their access to specific city areas. In [15], the itinerary planning problem is addressed with a deep variational Q-network that includes a reward scheme of route score, similar to our approach, but it considers the user's preferences to compute a personalized tour. A multi-objective reinforcement learning approach for trip recommendation is proposed in [16] that considers dynamic user preferences and the trip diversity and popularity that may enhance the user travel experience. In [17], an interactive multi-objective framework is introduced to generate personalized tourist walking itineraries, with the objective of minimizing the total distance and maximizing user satisfaction when visiting the selected PoIs. The itinerary planning problem is formulated according to the multi-objective prize-collecting vehicle routing problem (MO-PCVRP), and the optimizations are performed by an algorithm that considers preferences progressively introduced during the optimization process. In [18], a framework named GRM-RTrip for personalized trip recommendation is proposed, where the trip consists of an ordered sequence of PoIs that maximize user satisfaction, considering time and geographical restrictions. It relies on a graph-based representation model to learn PoI-PoI transition probability using graph networks. A Q-based reinforcement learning algorithm is used to compute the personalized trip. In [19], a deep neural network approach is proposed to compute travel route planning as a prediction of a sequence of PoIs. The prediction relies on learning user preferences, PoI attributes, and historical route data. The proposed approach can be applied to generate three types of planning, i.e., next-point recommendation, general route planning, and must-visit planning.