The reciprocity gradient allows agents to learn near-optimal context-sensitive policies by analytically propagating reward gradients through reputation chains in multi-agent settings.
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FairST combines 1D/2D/3D convolutions with fairness regularization using novel region-based and individual-based fairness gap metrics, reducing fairness gaps over 80% while improving accuracy over LSTMs, ConvLSTMs, and 3D CNNs on bike and ride share data.
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FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems
FairST combines 1D/2D/3D convolutions with fairness regularization using novel region-based and individual-based fairness gap metrics, reducing fairness gaps over 80% while improving accuracy over LSTMs, ConvLSTMs, and 3D CNNs on bike and ride share data.