{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T15:33:20Z","timestamp":1767713600127,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2019s infrastructure. An experimental evaluation was conducted considering Naples\u2019s fourteen most attractive points of interest. Results show that, with 30 state variables and more than 19\u22171012 possible states to be explored, the DRL-based planner converges to an optimal solution after only 20,000 learning steps.<\/jats:p>","DOI":"10.3390\/a16080362","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T07:58:52Z","timestamp":1690531132000},"page":"362","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8626-5805","authenticated-orcid":false,"given":"Claudia","family":"Di Napoli","sequence":"first","affiliation":[{"name":"Institute for High Performance Computing and Networking, National Research Council of Italy, 80131 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3580-9232","authenticated-orcid":false,"given":"Giovanni","family":"Paragliola","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking, National Research Council of Italy, 80131 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3266-9617","authenticated-orcid":false,"given":"Patrizia","family":"Ribino","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking, National Research Council of Italy, 90146 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0077-1799","authenticated-orcid":false,"given":"Luca","family":"Serino","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking, National Research Council of Italy, 80131 Naples, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"ref_1","first-page":"57","article-title":"Port cities and urban waterfront: Transformations and opportunities","volume":"2","author":"Giovinazzi","year":"2009","journal-title":"TeMA-J. 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