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Neurorobotic reinforcement learning for domains with parametrical uncertainty
Neuromorphic hardware paired with brain-inspired learning strategies have enormous potential for robot control. Explicitly, these advantages include low energy consumption, low latency, and adaptability. Therefore, developing and improving learning strategies, algorithms, and neuromorphic hardware i...
Autores principales: | Amaya, Camilo, von Arnim, Axel |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642204/ https://www.ncbi.nlm.nih.gov/pubmed/37965072 http://dx.doi.org/10.3389/fnbot.2023.1239581 |
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