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Deep Reinforcement Learning

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Deep Reinforcement Learning

Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achiev…

2016年6月17日

“Reinforcement Learning Identifies Age-Related Balance Strategy Shifts” by Huiyi Wang, Jozsef Kovecses, and Guillaume Durandau (McGill University & Jewish Rehabilitation Hospital, Canada). This study in TNSRE introduces a full-body musculoskeletal model with 290 muscles and 23 degrees of freedom to simulate human balance under muscle weakness conditions such as sarcopenia. Using reinforcement learning, curriculum learning, and muscle synergy representations, the model autonomously learned to rep

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