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model-based reinforcement learning

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https://www.readbyqxmd.com/read/29652591/a-reinforcement-learning-neural-network-for-robotic-manipulator-control
#1
Yazhou Hu, Bailu Si
We propose a neural network model for reinforcement learning to control a robotic manipulator with unknown parameters and dead zones. The model is composed of three networks. The state of the robotic manipulator is predicted by the state network of the model, the action policy is learned by the action network, and the performance index of the action policy is estimated by a critic network. The three networks work together to optimize the performance index based on the reinforcement learning control scheme. The convergence of the learning methods is analyzed...
April 13, 2018: Neural Computation
https://www.readbyqxmd.com/read/29621556/from-fear-to-confidence-changing-providers-attitudes-about-pediatric-palliative-and-hospice-care
#2
Tamara Vesel, Christiana Beveridge
CONTEXT: Children have limited access to hospice care: few existing hospice programs have dedicated pediatric teams, and adult hospice providers feel inadequately trained to care for children. OBJECTIVES: The aim of this study is to increase access to pediatric hospice care by empowering adult hospice providers to care for children through a comprehensive education program. Education empowers providers by changing their attitudes from inadequacy to confidence. METHODS: The authors developed a two-day education program to train interdisciplinary teams of adult hospice providers in pediatric care...
April 2, 2018: Journal of Pain and Symptom Management
https://www.readbyqxmd.com/read/29617217/emphasizing-the-positive-in-positive-reinforcement-using-non-binary-rewarding-for-training-monkeys-on-cognitive-tasks
#3
Benjamin Fischer, Detlef Wegener
Non-human primates constitute an indispensable model system for studying higher brain func-tions at the neurophysiological level. Studies involving these animals elucidated the neuronal mechanisms of various cognitive and executive functions, such as visual attention, working memory, and decision-making. Positive Reinforcement Training (PRT) constitutes the gold standard for training animals on the cognitive tasks employed in these studies. In the laboratory, PRT is usually based on applying a liquid reward as the reinforcer to strengthen the desired be-havior, and absence of the reward if the animal's response was wrong...
April 4, 2018: Journal of Neurophysiology
https://www.readbyqxmd.com/read/29608118/a-global-health-training-model-for-teaching-pediatric-clinical-decision-making-skills-to-rwandan-physical-therapists-a-case-report
#4
Kathryn Clark, Cara N Whalen Smith, Lori Kohls, Ines Musabyemariya, Egide Kayonga Ntagungira, Monika Mann, Steve R Fisher
BACKGROUND AND PURPOSE: There is increasing interest among physical therapists from high-income countries to participate in education development projects in low-income countries. However, there are few examples in the literature of effective developmental models or projects. This case report describes a model for improving pediatric clinical decision making skills among Rwandan physical therapists using best practices in clinical decision making, evidence-based practice where possible, and use of the International Classification of Functioning and Disability (ICF) model...
April 2, 2018: Physiotherapy Theory and Practice
https://www.readbyqxmd.com/read/29601060/revealing-neurocomputational-mechanisms-of-reinforcement-learning-and-decision-making-with-the-hbayesdm-package
#5
Woo-Young Ahn, Nathaniel Haines, Lei Zhang
Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG)...
October 2017: Comput Psychiatr
https://www.readbyqxmd.com/read/29599108/lessons-learned-from-a-living-lab-on-the-broad-adoption-of-ehealth-in-primary-health-care
#6
Ilse Catharina Sophia Swinkels, Martine Wilhelmina Johanna Huygens, Tim M Schoenmakers, Wendy Oude Nijeweme-D'Hollosy, Lex van Velsen, Joan Vermeulen, Marian Schoone-Harmsen, Yvonne Jfm Jansen, Onno Cp van Schayck, Roland Friele, Luc de Witte
BACKGROUND: Electronic health (eHealth) solutions are considered to relieve current and future pressure on the sustainability of primary health care systems. However, evidence of the effectiveness of eHealth in daily practice is missing. Furthermore, eHealth solutions are often not implemented structurally after a pilot phase, even if successful during this phase. Although many studies on barriers and facilitators were published in recent years, eHealth implementation still progresses only slowly...
March 29, 2018: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/29569637/towards-distribution-based-control-of-social-networks
#7
Dave McKenney, Tony White
Background: Complex networks are found in many domains and the control of these networks is a research topic that continues to draw increasing attention. This paper proposes a method of network control that attempts to maintain a specified target distribution of the network state. In contrast to many existing network control research works, which focus exclusively on structural analysis of the network, this paper also accounts for user actions/behaviours within the network control problem...
2018: Computational social networks
https://www.readbyqxmd.com/read/29569445/adversarial-threshold-neural-computer-for-molecular-de-novo-design
#8
Evgeny Putin, Arip Asadulaev, Quentin Vanhaelen, Yan Ivanenkov, Anastasiya Aladinskaya, Alex Aliper, Alex Zhavoronkov
In this paper, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial net- work architecture and reinforcement learning. ATNC uses a Differentiable Neural Com- puter as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions)...
March 23, 2018: Molecular Pharmaceutics
https://www.readbyqxmd.com/read/29535600/neuromodulated-synaptic-plasticity-on-the-spinnaker-neuromorphic-system
#9
Mantas Mikaitis, Garibaldi Pineda García, James C Knight, Steve B Furber
SpiNNaker is a digital neuromorphic architecture, designed specifically for the low power simulation of large-scale spiking neural networks at speeds close to biological real-time. Unlike other neuromorphic systems, SpiNNaker allows users to develop their own neuron and synapse models as well as specify arbitrary connectivity. As a result SpiNNaker has proved to be a powerful tool for studying different neuron models as well as synaptic plasticity-believed to be one of the main mechanisms behind learning and memory in the brain...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/29497060/frontal-cortex-function-as-derived-from-hierarchical-predictive-coding
#10
William H Alexander, Joshua W Brown
The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex...
March 1, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29485481/a-lack-of-continuity-in-education-training-and-practice-violates-the-do-no-harm-principle
#11
Robert Englander, Carol Carraccio
The paradigm shift to competency-based medical education (CBME) is under way, but incomplete implementation is blunting the potential impact on learning and patient outcomes. The fundamental principles of CBME call for standardizing outcomes addressing population health needs, then allowing time-variable progression to achieving them. Operationalizing CBME principles requires continuity within and across phases of the education, training, and practice continuum. However, the piecemeal origin of the phases of the "continuum" has resulted in a sequence of undergraduate to graduate medical education to practice that may be continuous temporally but bears none of the integration of a true continuum...
March 2018: Academic Medicine: Journal of the Association of American Medical Colleges
https://www.readbyqxmd.com/read/29483353/culture-morality-and-individual-differences-comparability-and-incomparability-across-species
#12
REVIEW
Gerard Saucier
Major routes to identifying individual differences (in diverse species) include studies of behaviour patterns as represented in language and neurophysiology. But results from these approaches appear not to converge on some major dimensions. Identifying dimensions of human variation least applicable to non-human species may help to partition human-specific individual differences of recent evolutionary origin from those shared across species. Human culture includes learned, enforced social-norm systems that are symbolically reinforced and referenced in displays signalling adherence...
April 19, 2018: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
https://www.readbyqxmd.com/read/29470489/integration-of-machine-learning-and-meta-analysis-identifies-the-transcriptomic-bio-signature-of-mastitis-disease-in-cattle
#13
Somayeh Sharifi, Abbas Pakdel, Mansour Ebrahimi, James M Reecy, Samaneh Fazeli Farsani, Esmaeil Ebrahimie
Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of '-omics' data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way...
2018: PloS One
https://www.readbyqxmd.com/read/29441027/pragmatically-framed-cross-situational-noun-learning-using-computational-reinforcement-models
#14
Shamima Najnin, Bonny Banerjee
Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered...
2018: Frontiers in Psychology
https://www.readbyqxmd.com/read/29431647/neural-mechanisms-for-adaptive-learned-avoidance-of-mental-effort
#15
Asako Mitsuto Nagase, Keiichi Onoda, Jerome Clifford Foo, Tomoki Haji, Rei Akaishi, Shuhei Yamaguchi, Katsuyuki Sakai, Kenji Morita
Humans tend to avoid mental effort. Previous studies have demonstrated this tendency using various demand-selection tasks; participants generally avoid options associated with higher cognitive demand. However, it remains unclear whether humans avoid mental effort adaptively in uncertain and non-stationary environments, and if so, what neural mechanisms underlie this learned avoidance and whether they remain the same irrespective of cognitive-demand types. We addressed these issues by developing novel demand-selection tasks where associations between choice options and cognitive-demand levels change over time, with two variations using mental arithmetic and spatial reasoning problems (29:4 and 18:2 males:females)...
February 5, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/29408398/selection-by-reinforcement-a-critical-reappraisal
#16
José E Burgos
This essay is a critical reappraisal of the idea of ontogenetic selection by reinforcement, according to which learning, specifically conditioning, in the individual animal is deeply analogous to phylogenetic evolution by natural selection. I focus on two general versions of this idea. The traditional Skinnerian version restricts the idea to operant conditioning and excludes Pavlovian conditioning, based on a sharp dichotomy between the two types of conditioning. The other version extends the idea to Pavlovian conditioning, based on a unified principle of reinforcement that applies to both types of conditioning, and linked to a neural-network model...
February 3, 2018: Behavioural Processes
https://www.readbyqxmd.com/read/29401733/an-active-learning-activity-to-reinforce-the-design-components-of-the-corticosteroids
#17
Stephen R Slauson, Prashant Mandela
Despite the popularity of active learning applications over the past few decades, few activities have been reported for the field of medicinal chemistry. The purpose of this study is to report a new active learning activity, describe participant contributions, and examine participant performance on the assessment questions mapped to the objective covered by the activity. In this particular activity, students are asked to design two novel corticosteroids as a group (6-8 students per group) based on the design characteristics of marketed corticosteroids covered in lecture coupled with their pharmaceutics knowledge from the previous semester and then defend their design to the class through an interactive presentation model...
February 5, 2018: Pharmacy (Basel, Switzerland)
https://www.readbyqxmd.com/read/29362024/models-of-nutrition-focused-continuing-education-programs-for-nurses-a-systematic-review-of-the-evidence
#18
Holly Mitchell, Catherine Lucas, Karen Charlton, Anne McMahon
Nurses are well-positioned to provide basic nutrition education and reinforce nutrition messages to patients in hospital and primary care settings. Despite this, nurses may not receive adequate training to provide this service, and there is limited opportunity for nurses to engage in nutrition-focused continuing education (CE). The aim of this review was to determine whether nurse nutrition education results in improved knowledge and practices; and explore which models of CE for nutrition may be most acceptable and effective in practice...
January 24, 2018: Australian Journal of Primary Health
https://www.readbyqxmd.com/read/29352006/machine-learning-in-cardiovascular-medicine-are-we-there-yet
#19
REVIEW
Khader Shameer, Kipp W Johnson, Benjamin S Glicksberg, Joel T Dudley, Partho P Sengupta
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform...
January 19, 2018: Heart: Official Journal of the British Cardiac Society
https://www.readbyqxmd.com/read/29346018/the-tortoise-and-the-hare-interactions-between-reinforcement-learning-and-working-memory
#20
Anne G E Collins
Learning to make rewarding choices in response to stimuli depends on a slow but steady process, reinforcement learning, and a fast and flexible, but capacity-limited process, working memory. Using both systems in parallel, with their contributions weighted based on performance, should allow us to leverage the best of each system: rapid early learning, supplemented by long-term robust acquisition. However, this assumes that using one process does not interfere with the other. We use computational modeling to investigate the interactions between the two processes in a behavioral experiment and show that working memory interferes with reinforcement learning...
January 18, 2018: Journal of Cognitive Neuroscience
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