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pro vyhledávání: '"M, Ugur"'
In visual Reinforcement Learning (RL), learning from pixel-based observations poses significant challenges on sample efficiency, primarily due to the complexity of extracting informative state representations from high-dimensional data. Previous meth
Externí odkaz:
http://arxiv.org/abs/2409.02714
Autor:
Sivakumar, Arun N., Gasparino, Mateus V., McGuire, Michael, Higuti, Vitor A. H., Akcal, M. Ugur, Chowdhary, Girish
We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($\sim 0.75$ m), degradation in RTK-GPS accura
Externí odkaz:
http://arxiv.org/abs/2404.17718
Autor:
Beaurain, Claude
Publikováno v:
Revue d'économie financière, 1992 Apr 01(20), 247-249.
Externí odkaz:
https://www.jstor.org/stable/42902986
Autor:
Crane, Howard
Publikováno v:
Turkish Studies Association Bulletin, 1999 Oct 01. 23(2), 37-39.
Externí odkaz:
https://www.jstor.org/stable/45434332
Autor:
Rahman, Tariq
Publikováno v:
Islamic Studies, 1999 Oct 01. 38(3), 469-470.
Externí odkaz:
https://www.jstor.org/stable/20837055
Automated lane change is one of the most challenging task to be solved of highly automated vehicles due to its safety-critical, uncertain and multi-agent nature. This paper presents the novel deployment of the state of art Q learning method, namely R
Externí odkaz:
http://arxiv.org/abs/2009.11905
Akademický článek
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Publikováno v:
IEEE Access, Vol 11, Pp 16843-16854 (2023)
In this paper, we present an advanced adaptive cruise control (ACC) concept powered by Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car-following policies. Unlike the current trend in developing DRL-based ACC sys
Externí odkaz:
https://doaj.org/article/b5a59a614b494475ba90104bae18adb6
Publikováno v:
In Measurement September 2021 182