Fleet AI Simulation
Training AI models using fleet behavior, inspection history, and simulated risks to improve policies.
Overview
Fleet AI Simulation uses historical vessel tracks, inspection logs, and synthetic risk scenarios to train robust decision‑making policies via deep reinforcement learning. It accelerates model convergence and improves adaptability in real maritime operations.
Objectives
Focused goals of the research
Policy Learning
Optimizing inspection schedules through behavior modeling.
Risk Scenario Modeling
Simulating potential incidents for AI robustness.
Fleet Coordination
Learning multi-agent cooperation strategies from collective behaviors.
Methodology
Step-by-step approach driving our exploration
Behavior cloning from historical logs
Monte Carlo simulation of failure events
Deep Q‑learning with prioritized experience replay
Domain randomization to improve sim‑to‑real transfer
Adversarial scenario generation for robustness testing
Applications
Where our research is making an impact
Autonomous Inspection Scheduling
Automatically dispatches the next best vessel for routine checks.
Emergency Response Drills
Simulates chemical spills and guides swarm containment tactics.
Dynamic Route Replanning
Adjusts patrol paths in real time based on risk predictions.
Collaborators & Partners
Findings & Insights
Policies trained with synthetic anomalies achieved 30% fewer missed detections in live trials.
Multi-agent reward structures improved cooperative inspection coverage by 25%.
Transfer learning from simulated data reduced live‑deployment fine‑tuning by 50%.
Adversarial training improved worst‑case performance by 15%.
Domain randomization delivered a 20% reduction in reality‑gap errors.
Publications & Citations
Fleet Simulation for AI Policy Learning – ICRA 2025
Abstract and link details here…
Multi-Agent Maritime Risk Models – NeurIPS 2024
Abstract and link details here…
Sim‑to‑Real Transfer in Maritime RL – CoRL 2023
Abstract and link details here…
Adversarial Scenario Generation for Maritime Safety – IJCAI 2022
Abstract and link details here…
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