[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82877-en":3,"doc-seo-82877-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82877,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","RL-Ballast Ship Ballast Water Path Planning and Clog Prediction via Reinforcement Learning","Shipping 4.0 drives autonomous and reduced-crew vessels to rely on intelligent internal control for safety and structural stability. Ballast-water management maintains trim and integrity, yet conventional rule-based and manual operation struggles with hydraulic anomalies such as valve failures or pipe blockages, often requiring dense sensing. RLBallast uses graph-based deep reinforcement learning for adaptive path planning and sensor-frugal blockage candidate scoring, converting valve permutations into feasible fluid routes and using frame-stacked observations, action masking, and failure-history ranking to enable rerouting and diagnosis under limited instrumentation. Simulation results validate full completion of unexpected single-blockage cases, fewer decision steps, and strong Top-k diagnostic accuracy.","RL-Ballast: Ship Ballast Water Path Planning and Clog Prediction via Reinforcement Learning  \nMing-Kuan Lin, Yi-Chung Lai, Ming-Hsin Chiang, Tsung-Wei Pan, Jung-Hua Wang*  \nAbstract—Under the Shipping 4.0 paradigm, autonomous and reduced-crew vessels require intelligent internal systems to ensure operational safety and structural stability. Ballast-water control is essential for maintaining ship trim and integrity, yet conventional rule-based logic and manual operations have limited adaptability to unexpected hydraulic anomalies, such as valve failures or pipe blockages, and often require dense pressure or flow sensors for diagnosis. To address these limitations, this paper proposes RLBallast, a graph-based deep reinforcement learning framework for adaptive ballast-water path planning and sensor-frugal blockage candidate scoring. The framework converts the valve-permutation problem into 54 feasible fluid-transfer routes generated by graph theory and depth-first search. It approximates the partially observable ballast environment using frame-stacked tank levels and action outcomes, enabling the agent to infer hidden blockage effects without explicitly modeling a high-dimensional POMDP. Episode-level failed-action memory and dynamic action masking are integrated during deterministic inference to prevent repeated ineffective actions and support immediate rerouting. Failed transfer histories are accumulated to rank suspicious valves or pipe segments without dense flow or pressure instrumentation. Monte Carlo evaluations in a simulated ballast system show that RLBallast completes all unexpected single-blockage scenarios. Compared with a Dijkstra rule-based baseline, it reduces the average decision steps from 61.0 to 41.5. For diagnostic support, the failure-history scoring scheme achieves a 100% Top-3(hit) rate, a 66.7% strict Top-1(hit) rate, and an 83.3% Top-1(tie-hit) rate under serially indistinguishable blockage conditions. These results indicate that RL-Ballast provides adaptive rerouting and maintenance-oriented blockage diagnosis under limited sensing conditions.  \nKeywords—Ballast Water Management, Deep Reinforcement Learning, Double DQN, Action Masking, Blockage Candidate Scoring, Maritime Autonomous Systems.  \nSubmitted on June. 30, 2026 for review, this work was supported in part by the Ship and Ocean Industries R&D Center (SOIC) . (Corresponding author: Jung-Hua Wang).  \nJung-Hua Wang is with the AI Research Center, National Taiwan Ocean University, Keelung 20224, Taiwan ([j](jhwang@email.ntou.edu.tw)[hwang@email.ntou.edu.tw](jhwang@email.ntou.edu.tw)).  \nMing-Hsin Chiang was with Department of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan. He is now with Shinsoft Corporation, Taiwan ([ac872xy@gmail.com](ac872xy@gmail.com)).  \nYi-Chung Lai was with Department of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan. He is now with AI Research Center (e[mail: ](mail: k8130488@gmail.com)[k8130488@gmail.com](mail: k8130488@gmail.com)).  \nMing-Kuan Lin is currently with Department of Electrical Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan (e-mail: [j](jacksonlin092@gmail.com)[acksonlin092@gmail.com](jacksonlin092@gmail.com)).  \nI. INTRODUCTION  \nUnder the Shipping 4.0 paradigm, the maritime industry is moving toward digitalized, intelligent, and increasingly autonomous vessel operations (Sullivan et al., 2021, 2020). Considerable progress has been made in autonomous navigation, collision avoidance, trajectory tracking, and route optimization. However, practical ship autonomy requires not only external navigational intelligence but also reliable control of internal onboard systems. These internal systems are critical for maintaining vessel safety, operational continuity, and reduced-crew ship management.  \nAmong onboard internal systems, the ballast-water piping system is a critical network of pipes, valves, and pumps on a vessel used to loa","cbCaip8rwgfOefW7","https://ap.wps.com/l/cbCaip8rwgfOefW7","pdf",1587502,1,18,"English","en",105,"# Introduction\n## Ballast-water control requirements\n## Limitations of rule-based and manual ballast operations\n## Need for sensor-frugal rerouting and diagnosis","[{\"question\":\"What problem does RL-Ballast address in ship ballast-water control?\",\"answer\":\"RL-Ballast addresses the difficulty of adaptively rerouting ballast-water flows when unexpected hydraulic anomalies occur, such as valve failures or pipe blockages, especially under limited sensor availability.\"},{\"question\":\"How does the framework plan ballast-water transfer routes under valve-permutation uncertainty?\",\"answer\":\"It transforms the valve-permutation problem into a set of feasible fluid-transfer routes generated via graph theory and depth-first search, then uses reinforcement learning to select routes in an environment approximated from stacked tank-level frames.\"},{\"question\":\"How does RL-Ballast diagnose likely blockage locations with sparse instrumentation?\",\"answer\":\"It accumulates episode-level failed-action transfer histories and applies a failure-history scoring scheme to rank suspicious valves or pipe segments, achieving high Top-k hit rates under serially indistinguishable blockage conditions.\"}]",1784183606,45,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"rl-ballast-ship-ballast-water-path-planning-and-clog-prediction-via-reinforcement-learning","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/rl-ballast-ship-ballast-water-path-planning-and-clog-prediction-via-reinforcement-learning/82877/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does RL-Ballast address in ship ballast-water control?","Question",{"text":75,"@type":76},"RL-Ballast addresses the difficulty of adaptively rerouting ballast-water flows when unexpected hydraulic anomalies occur, such as valve failures or pipe blockages, especially under limited sensor availability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework plan ballast-water transfer routes under valve-permutation uncertainty?",{"text":80,"@type":76},"It transforms the valve-permutation problem into a set of feasible fluid-transfer routes generated via graph theory and depth-first search, then uses reinforcement learning to select routes in an environment approximated from stacked tank-level frames.",{"name":82,"@type":73,"acceptedAnswer":83},"How does RL-Ballast diagnose likely blockage locations with sparse instrumentation?",{"text":84,"@type":76},"It accumulates episode-level failed-action transfer histories and applies a failure-history scoring scheme to rank suspicious valves or pipe segments, achieving high Top-k hit rates under serially indistinguishable blockage conditions.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & 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