RL-Ballast: Ship Ballast Water Path Planning and Clog Prediction via Reinforcement Learning
2026-07-06 • Machine Learning
Machine Learning
AI summaryⓘ
The authors present RL-Ballast, a smart system using deep reinforcement learning and graph theory to manage ballast-water flow in ships with fewer sensors. Unlike traditional methods, their approach can adapt to problems like valve failures or pipe blockages by planning alternative water routes and learning from past failed attempts. This helps the system quickly find solutions and also identify which parts might be blocked, even without many sensors. Their tests show RL-Ballast works better than traditional rule-based methods, making ship operations safer and more efficient.
Ballast-water controlReinforcement learningGraph theoryValve permutationHydraulic blockagePath planningPartially Observable Markov Decision Process (POMDP)Sensor-frugal diagnosisMonte Carlo simulationShipping 4.0
Authors
Ming-Kuan Lin, Yi-Chung Lai, Ming-Hsin Chiang, Tsung-Wei Pan, Jung-Hua Wang
Abstract
Under the Shipping 4.0 paradigm, autonomous and reduced-crew vessels require intelligent internal systems to maintain operational safety and structural stability. Ballast-water control is essential for ship trim and integrity, but conventional rule-based or manual approaches have limited adaptability to hydraulic anomalies such as valve failures and pipe blockages, and often depend on dense pressure or flow sensors for diagnosis. To address these limitations, this paper proposes RL-Ballast, a graph-based deep reinforcement learning framework for adaptive ballast-water path planning and sensor-frugal blockage candidate scoring. The valve-permutation problem is transformed into 54 feasible fluid-transfer routes generated using graph theory and depth-first search. The partially observable ballast environment is approximated with frame-stacked tank levels and action outcomes, allowing the agent to infer hidden blockage effects without explicitly modeling a high-dimensional POMDP. During deterministic inference, episode-level failed-action memory and dynamic action masking prevent repeated ineffective actions and support immediate rerouting. Failed transfer histories are further accumulated to rank suspicious valves or pipe segments without dense instrumentation. Monte Carlo simulations show that RL-Ballast completes all unexpected single-blockage scenarios and reduces average decision steps from 61.0 to 41.5 compared with a Dijkstra rule-based baseline. 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 suggest that RL-Ballast enables adaptive rerouting and maintenance-oriented blockage diagnosis under limited sensing conditions.