Off-Policy Reinforcement Learning for Robotics
Nowadays, industrial processes are vastly automated by means of robotic manipulators. In some cases, robots occupy a large fraction of the production line, performing a rich range of tasks. In contrast to their tireless ability to repeatedly perform the same tasks with millimetric precision, curren...
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| Format: | Others |
| Language: | en |
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2021
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| Online Access: | https://tuprints.ulb.tu-darmstadt.de/17536/1/thesis.pdf Tosatto, Samuele <http://tuprints.ulb.tu-darmstadt.de/view/person/Tosatto=3ASamuele=3A=3A.html> (2021): Off-Policy Reinforcement Learning for Robotics. (Publisher's Version)Darmstadt, Technische Universität, DOI: 10.26083/tuprints-00017536 <https://doi.org/10.26083/tuprints-00017536>, [Ph.D. Thesis] |
Internet
https://tuprints.ulb.tu-darmstadt.de/17536/1/thesis.pdfTosatto, Samuele <http://tuprints.ulb.tu-darmstadt.de/view/person/Tosatto=3ASamuele=3A=3A.html> (2021): Off-Policy Reinforcement Learning for Robotics. (Publisher's Version)Darmstadt, Technische Universität, DOI: 10.26083/tuprints-00017536 <https://doi.org/10.26083/tuprints-00017536>, [Ph.D. Thesis]