MADDPG
PulseAugur coverage of MADDPG — every cluster mentioning MADDPG across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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Deep Reinforcement Learning applied to Bus Fleets and High-Speed Flows
Two recent arXiv papers explore the application of deep reinforcement learning (DRL) in complex control scenarios. The first paper details a single-agent RL framework for bus fleet control, which reformulates the proble…
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Decentralized HVAC control agents optimize energy use on edge hardware
Researchers have developed a decentralized active inference agent system for controlling multi-zone HVAC systems, designed to optimize energy consumption and manage peak power demand. Each zone is equipped with an indep…
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New OGR-MARL framework enhances USV cooperative pursuit capabilities
Researchers have developed OGR-MARL, a novel framework for multi-agent reinforcement learning designed for cooperative pursuit scenarios involving heterogeneous unmanned surface vehicles (USVs) in constrained port water…
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New DRL Framework Optimizes Urban EV Fleet Control
Researchers have developed a new framework for controlling urban electric vehicle (EV) fleets that uses distributionally robust reinforcement learning (DRL) to handle uncertain demand and travel times. This approach, ca…
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Researchers develop semi-Markov RL for city-scale EV ride-hailing
Researchers have developed a novel semi-Markov reinforcement learning approach for optimizing city-scale electric vehicle (EV) ride-hailing fleets. This method addresses complex decisions like dispatch, repositioning, a…
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Researchers develop semi-Markov RL for EV ride-hailing, boosting profits and ensuring feasibility.
Researchers have developed a novel Semi-Markov Reinforcement Learning approach for managing large-scale electric vehicle ride-hailing fleets. This method ensures that dispatch, repositioning, and charging decisions stri…