Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ashwini, S.R.; Pooja, M.; Jayapriya, J.; Vinay, M.; Savitha, Margaret; Deepa, S. Title A name given to the resource Trajectories forSpace Missions: Bridging Tradition andInnovation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1670 LNNS;pp.427-439 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-3486-9_32" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-3486-9_32</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030280271?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030280271?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ashwini S.R., Christ University, Karnataka, Bangalore, India; Pooja M., Christ University, Karnataka, Bangalore, India; Jayapriya J., Christ University, Karnataka, Bangalore, India; Vinay M., Christ University, Karnataka, Bangalore, India; Savitha M., Christ University, Karnataka, Bangalore, India; Deepa S., Christ University, Karnataka, Bangalore, India Description An account of the resource Spacecraft trajectory optimization has always been a determining factor in successful space missions as it should be precise and efficient in automatically exploiting new opportunities present in the complex and dynamic environment. Traditional optimization algorithms cannot meet the increasing demand for fast computation, adaptation ability, or overcoming real-time constraints. A recently developed technique called reinforcement learning is quite promising in dealing with such issues by proposing innovative solutions for trajectory optimization. This paper surveys cutting-edge reinforcement learning solutions for optimizing spacecraft trajectory problems. Comprehensive and pragmatic analysis based on different aspects of currently available solutions, and concise reports are generated to get the latest update on this field, as well as provide reference on designing future-related solutions. The survey suggests that more efforts from the research field should be spent on reinforcement learning solutions especially when applied in the real mission scenario because there are still many challenges unattended by the community that were pointed out before being delivered at the end-user level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Adaptive learning; Convex optimization; Deep reinforcement learning (DRL); Heuristic approach; Hybrid approach Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981953485-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online