A retrieval-augmented Transformer predicts multi-step port-of-call sequences in global shipping, reporting 72.3% first-destination accuracy and 61.4% three-step accuracy while outperforming CatBoost and LSTM baselines.
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ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
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A Retrieval-Enhanced Transformer for Multi-Step Port-of-Call Sequence Prediction in Global Liner Shipping
A retrieval-augmented Transformer predicts multi-step port-of-call sequences in global shipping, reporting 72.3% first-destination accuracy and 61.4% three-step accuracy while outperforming CatBoost and LSTM baselines.
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ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.