Admiral-Driven ML Framework for Marine Operations
summary
Claims an IRL-based metric learning pipeline recovers admiral-level reward functions from demonstrations plus AB-style preference comparisons and sparse annotations, then feeds those rewards into constrained multi-objective optimizers balancing fuel, mission success probability and platform survivability. Implementation uses a simple alpha/beta/gamma loss combining hard constraints (rule checks) and soft preferences, occasional PyTorch components, manual sequential decomposition, iterative preference-refinement loops and off-policy plus deep reinforcement learning for generalization. Results show linear solvers often suffice, hybrid human-in-the-loop alignment reduces surprising unsafe behaviors, and safety gates plus active learning mitigate data sparsity.
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chapters (13)
- 0:00Introduction
- 1:13AI challenge for admirals
- 3:00AI solutions vs admirals
- 6:15Maritime AI challenges
- 8:02Six steps to model building
- 11:13Admiral preference concept
- 13:46Explainability importance
- 16:40Complex loss function
- 18:04Admirals and the Pentagon
- 20:15Feedback loop and solution assembly
- 22:45Model training in practice
- 24:30System safety and reliability
- 26:47Summary
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