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Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets
A key revelation of our research is the feasibility of sampling training data for large traffic systems from simulations conducted on smaller traffic systems.
Iterative Forgetting: Online Data Stream Regression Using Database-Inspired Adaptive Granulation
Many modern systems, such as financial, transportation, and telecommunications systems, are time-sensitive in the sense that they demand low-latency predictions for real-time decision-making.
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior
In this work, we integrate `social' interactions into the MARL setup through a user-defined relational network and examine the effects of agent-agent relations on the rise of emergent behaviors.
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