Mixed-methods research shows collective care practices are constrained by personal, relational, technological, and structural factors in existing PHI systems, leading to the CC-Proact operational map with three design levers and ten recommendations for collective health informatics.
Reward shaping in multiagent reinforcement learning for self-organizing systems in assembly tasks
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Transformer-guided DRL cut training time steps to 25% of vanilla DRL while reaching 97.2% of optimal energy consumption for eVTOL takeoff versus 96.1%.
citing papers explorer
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Unpacking "Personal" Health Informatics for Proactive Collective Care
Mixed-methods research shows collective care practices are constrained by personal, relational, technological, and structural factors in existing PHI systems, leading to the CC-Proact operational map with three design levers and ten recommendations for collective health informatics.
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Transformer-Guided Deep Reinforcement Learning for Optimal Takeoff Trajectory Design of an eVTOL Drone
Transformer-guided DRL cut training time steps to 25% of vanilla DRL while reaching 97.2% of optimal energy consumption for eVTOL takeoff versus 96.1%.