Results 71 to 80 of about 580,002 (195)
Edge- and vertex-reinforced random walks with super-linear reinforcement on infinite graphs [PDF]
arXiv, 2015 In this paper we introduce a new simple but powerful general technique for the study of edge- and vertex-reinforced processes with super-linear reinforcement, based on the use of order statistics for the number of edge, respectively of vertex, traversals.
arxiv
Urban LifelineBuried pipeline systems are vulnerable to joint damage in earthquakes. Previous studies have shown that bellows joints are effective in increasing deformation capacity and reducing the axial force of the main pipeline.
Fang-fang Li +5 more
doaj +1 more source
Lineage Evolution Reinforcement Learning [PDF]
arXiv, 2020 We propose a general agent population learning system, and on this basis, we propose lineage evolution reinforcement learning algorithm. Lineage evolution reinforcement learning is a kind of derivative algorithm which accords with the general agent population learning system. We take the agents in DQN and its related variants as the basic agents in the
arxiv
New Phytologist, 2013 SummaryA major goal of evolutionary biology is to understand how diverging populations become species. The evolution of reproductive isolation (RI) halts the genomic homogenization caused by gene flow and recombination, and enables differentiation and local adaptations to become fixed between newly forming species. Selection can favor the strengthening
openaire +3 more sources
Deep Reinforcement Learning for Conversational AI [PDF]
arXiv, 2017 Deep reinforcement learning is revolutionizing the artificial intelligence field. Currently, it serves as a good starting point for constructing intelligent autonomous systems which offer a better knowledge of the visual world. It is possible to scale deep reinforcement learning with the use of deep learning and do amazing tasks such as use of pixels ...
arxiv
Role of nitric oxide in psychostimulant-induced neurotoxicity
AIMS Neuroscience, 2019 In recent decades, consumption of psychostimulants has been significantly increased all over the world, while exact mechanisms of neurochemical effects of psychomotor stimulants remained unclear. It is assumed that the neuronal messenger nitric oxide (NO)
Valentina Bashkatova, Athineos Philippu
doaj +1 more source
Transfer Learning in Deep Reinforcement Learning: A Survey [PDF]
arXiv, 2020 Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning ...
arxiv
REINFORCEMENT OF INHIBITION [PDF]
Journal of the Experimental Analysis of Behavior, 1983 A differential‐reinforcement‐of‐other‐behavior (DRO) schedule with trials and delayed reinforcement was investigated. Periodically a wheel was briefly available to rats, followed six seconds later by brief availability of a bar. Variable‐ratio food reinforcement of wheel turns was adjusted to give 95% turns.
openaire +3 more sources
Health Research Policy and Systems, 2017 Background In Burkina Faso, malaria remains the primary cause of healthcare use, morbidity and child mortality. Therefore, efforts are needed to support the knowledge transfer and application of the results of numerous studies to better formulate and ...
Esther Mc Sween-Cadieux +3 more
doaj +1 more source
Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning [PDF]
arXiv, 2019 Reinforcement learning, evolutionary algorithms and imitation learning are three principal methods to deal with continuous control tasks. Reinforcement learning is sample efficient, yet sensitive to hyper-parameters setting and needs efficient exploration; Evolutionary algorithms are stable, but with low sample efficiency; Imitation learning is both ...
arxiv