The paper formalizes a hybrid quantum-classical architectural style and demonstrates a method that identifies decision boundaries for selecting configurations based on user QoS criteria.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.
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Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.