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Bootstrapping of Parameterized Skills Through Hybrid Optimization in Task and Policy Spaces
Modern robotic applications create high demands on adaptation of actions with respect to variance in a given task. Reinforcement learning is able to optimize for these changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. We propose a parame...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805929/ https://www.ncbi.nlm.nih.gov/pubmed/33500934 http://dx.doi.org/10.3389/frobt.2018.00049 |