Multi-objective route selection for ice-class vessels using reinforcement learning and graph-based approaches
Bibliographic record
Abstract
Route selection for ships in ice is a complicated problem in marine navigation. The navigators have to optimize many economic and environmental factors of the routes while adhering to all maritime regulations to ensure safety. The International Maritime Organization has introduced the Polar Operational Limit Assessment Risk Indexing System (POLARIS) as guidelines for all vessels operating in the Arctic Ocean. This research investigates a framework for finding an optimal route for different ice-class vessels using two methods: graph-based approaches and reinforcement learning. The system uses ice charts from the Canadian Ice Service to explore possible routes in a grid world. Reward and cost functions are formulated to achieve operational objectives, such as optimizing the distance travelled, voyage time, and fuel consumption while complying with POLARIS regulation. The graph-based method surpasses the Q-learning in deterministic cases. Despite the shortcoming of not handling the non-deterministic environment, it also shows similar routes compared to Q-learning in a stochastic context. The trial results show that the framework provides a means to identify an optimal route for vessels navigating through ice-covered waters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".