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An Architecture for Unattended Containerized (Deep) Reinforcement Learning with Webots
March 5, 2024, 2:42 p.m. | Tobias Haubold, Petra Linke
cs.LG updates on arXiv.org arxiv.org
Abstract: As data science applications gain adoption across industries, the tooling landscape matures to facilitate the life cycle of such applications and provide solutions to the challenges involved to boost the productivity of the people involved. Reinforcement learning with agents in a 3D world could still face challenges: the knowledge required to use a simulation software as well as the utilization of a standalone simulation software in unattended training pipelines.
In this paper we review tools …
abstract adoption agents applications architecture arxiv boost challenges cs.ai cs.lg cs.ro data data science industries landscape life life cycle people productivity reinforcement reinforcement learning science solutions tooling type world
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