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Human Reinforcement Learning Subdivides Structured Action Spaces by Learning Effector-Specific Values
Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved q...
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| Auteurs principaux: | , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Society for Neuroscience
2009
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2796632/ https://ncbi.nlm.nih.gov/pubmed/19864565 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1523/JNEUROSCI.2469-09.2009 |
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