A new multi-stage sequential model with selective classifiers is proposed to characterize agent actions and design sequences that incentivize genuine improvement rather than gaming in strategic classification.
A multi- agent reinforcement learning approach to promote cooperation in evolutionary games on networks with environmental feed- back,
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Integrates multi-head attention with SAC for faster convergence in optimizing additive manufacturing parameters to minimize porosity, outperforming DQN, PPO, TD3, and vanilla SAC.
citing papers explorer
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Sequential Strategic Classification with Multi-Stage Selective Classifiers
A new multi-stage sequential model with selective classifiers is proposed to characterize agent actions and design sequences that incentivize genuine improvement rather than gaming in strategic classification.
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Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
Integrates multi-head attention with SAC for faster convergence in optimizing additive manufacturing parameters to minimize porosity, outperforming DQN, PPO, TD3, and vanilla SAC.