Zobrazeno 1 - 10
of 3 359
pro vyhledávání: '"imitation learning"'
Autor:
LI Dong, XU Xiao, WU Lin
Publikováno v:
Zhihui kongzhi yu fangzhen, Vol 46, Iss 2, Pp 18-23 (2024)
To study the intelligent decision making methods under limited decision samples, aiming at the problems that operational decision-making experience is difficult to express and the training samples for intelligent decision learning are limited, based
Externí odkaz:
https://doaj.org/article/7b00f579689645de8be79c85f9a8fbbf
Publikováno v:
IEEE Access, Vol 12, Pp 85859-85879 (2024)
Vehicle path planning is one of the effective ways to relieve the huge traffic flow pressure of modern urban transportation system, and it is also an important way to realize carbon emission reduction and to build green transportation system as well
Externí odkaz:
https://doaj.org/article/d82f7f04b88446808adb276013b1377d
Autor:
Habibian, Soheil
The growing presence of modern learning robots necessitates a fundamental shift in design, as these robots must learn skills from human inputs. Two main components close the loop in a human-robot interaction: learning and communication. Learning deri
Externí odkaz:
https://hdl.handle.net/10919/120698
Safe dynamic optimization of automatic generation control via imitation-based reinforcement learning
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
IntroductionThe increasing penetration of distributed generation (e.g., solar power and wind power) in the energy market has caused unpredictable disturbances in power systems and accelerated the application of intelligent control, such as reinforcem
Externí odkaz:
https://doaj.org/article/0457dd06bb9e4992907b1d063b4a8613
Publikováno v:
CLEI Electronic Journal, Vol 27, Iss 2 (2024)
In light of escalating concerns over climate change, harnessing oceanic data becomes increasingly urgent. Oceans serve as linchpins in understanding the intricate dynamics governing climate phenomena, exerting pivotal influence over global weather pa
Externí odkaz:
https://doaj.org/article/a37fae6495cb481792b228c167a7547d
Publikováno v:
IEEE Access, Vol 12, Pp 114552-114572 (2024)
Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial and error, to solve complex decision-making problems. Learning with little supervision causes RL methods to require large amounts of data, rendering them too e
Externí odkaz:
https://doaj.org/article/9dd7e863921647afbb7de0a3f4588be2
Publikováno v:
IEEE Access, Vol 12, Pp 76194-76206 (2024)
This paper investigates the high-level decision-making problem in highway scenarios regarding lane changing and over-taking other slower vehicles. In particular, this paper aims to improve the Travel Assist feature for automatic overtaking and lane c
Externí odkaz:
https://doaj.org/article/488c1512104f4f58b9ff53350a9aa187
Publikováno v:
IEEE Access, Vol 12, Pp 65117-65127 (2024)
Imitation learning is a widely-used paradigm for decision making that learns from expert demonstrations. Existing imitation algorithms often require multiple interactions between the agent and the environment from which the demonstration is obtained.
Externí odkaz:
https://doaj.org/article/958aa6a3f3a3430e9304b237f49baf7d
Publikováno v:
IEEE Open Journal of the Industrial Electronics Society, Vol 5, Pp 91-108 (2024)
Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectivene
Externí odkaz:
https://doaj.org/article/54dc882de70b4b6b9d2af31b0fb21b09
Autor:
Jonnavittula, Ananth
As robots transition from controlled environments, such as industrial settings, to more dynamic and unpredictable real-world applications, the need for adaptable and robust learning methods becomes paramount. In this dissertation we develop Interacti
Externí odkaz:
https://hdl.handle.net/10919/120552