Results 81 to 90 of about 580,002 (195)

ON THE CLASSIFICATION OF REINFORCEMENT SCHEDULES [PDF]

, 1956
W. N. Schoenfeld, William W. Cumming, Eliot Hearst   +2 more
openalex   +1 more source

Reinforced random walk [PDF]

arXiv, 2012
A survey of reinforced random walk, with emphasis on the linear case.
arxiv  

Counterexample-Guided Repair of Reinforcement Learning Systems Using Safety Critics [PDF]

arXiv
Naively trained Deep Reinforcement Learning agents may fail to satisfy vital safety constraints. To avoid costly retraining, we may desire to repair a previously trained reinforcement learning agent to obviate unsafe behaviour. We devise a counterexample-guided repair algorithm for repairing reinforcement learning systems leveraging safety critics. The
arxiv  

Hierarchical Modeling of Multifunctional Novel Carbon Nanotube Reinforced Hybrid Composites for Next Generation Polymeric Composites [PDF]

arXiv, 2017
This article provides an overview of the modeling of the effective thermomechanical properties of the multifunctional carbon nanotube (CNT) reinforced hybrid composites for advanced structural applications. The novel constructional feature of such multifunctional CNT-reinforced hybrid composite is that CNTs are radially grown on the circumferential ...
arxiv  

INTER‐RESPONSE TIME DISTRIBUTION AS A FUNCTION OF DIFFERENTIAL REINFORCEMENT OF TEMPORALLY SPACED RESPONSES [PDF]

, 1959
Roger T. Kelleher, William Fry, Leonard Cook   +2 more
openalex   +1 more source

Reinforced Concrete [PDF]

Proceedings of the Institution of Mechanical Engineers, 1909
n ...
openaire   +2 more sources

Research on Post Fire Detection and Evaluation of Liu Jiang the Yellow River Bridge

E3S Web of Conferences, 2019
After the bridge is damaged by fire, it is necessary to quickly probe the situation of the damaged structure, to take a response as soon as possible to ensure the safe passage of the bridge.
Zeng Yong, Ji Meng-Long, Huang Shan-Feng
doaj   +1 more source

How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review

Mayo Clinic Proceedings: Digital Health
To assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors.
Amy Bucher, PhD, E. Susanne Blazek, PhD, Christopher T. Symons, PhD   +2 more
doaj  

Quantitative Evaluation of Internal Pavement Distresses Based on 3D Ground Penetrating Radar

The Baltic Journal of Road and Bridge Engineering
Asphalt pavement will inevitably produce internal distresses during service, which increases the risk of deterioration of pavement structural performance.
Yong Liu, Zhiwen Zhang, Yang Yuan, Yunsheng Zhu, Kaifeng Wang   +4 more
doaj   +1 more source

Asymptotic properties of a multicolored random reinforced urn model with an application to multi-armed bandits [PDF]

arXiv
The random self-reinforcement mechanism, characterized by the principle of ``the rich get richer'', has demonstrated significant utility across various domains. One prominent model embodying this mechanism is the random reinforcement urn model. This paper investigates a multicolored, multiple-drawing variant of the random reinforced urn model.
arxiv