780 research outputs found
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks
Reward engineering is an important aspect of reinforcement learning. Whether
or not the user's intentions can be correctly encapsulated in the reward
function can significantly impact the learning outcome. Current methods rely on
manually crafted reward functions that often require parameter tuning to obtain
the desired behavior. This operation can be expensive when exploration requires
systems to interact with the physical world. In this paper, we explore the use
of temporal logic (TL) to specify tasks in reinforcement learning. TL formula
can be translated to a real-valued function that measures its level of
satisfaction against a trajectory. We take advantage of this function and
propose temporal logic policy search (TLPS), a model-free learning technique
that finds a policy that satisfies the TL specification. A set of simulated
experiments are conducted to evaluate the proposed approach
Describing the Dutch Social Networks and Fertility Study and how to process it
BACKGROUND The social networks of people play a prominent role in theories on fertility. Investigating how networks shape behaviour is hard, because of the difficulty in measuring (large) networks among representative samples. Therefore, comprehensive studies of the variation in the structure and composition of networks and their impact on fertility outcomes are lacking. OBJECTIVE I aim to, first, describe the Dutch Social Networks and Fertility Study, and, second, describe the R-package FertNet that processes data from this study and transforms it into an easy-to-use format for researchers. METHODS The data used are from the Longitudinal Internet Social Survey (LISS) panel, a representative panel of Dutch households. The focus is on the Social Networks and Fertility Study that includes a subsample of women between the ages of 18â40. Specific survey software was designed to capture each respondentâs personal network comprising 25 individuals with whom they had a relationship. In total, 758 women reported on over 18,750 relationships. For each person with whom the respondent had a relationship, several questions were asked about fertility-related topics. Uniquely, the connections between these people were also assessed. The R-package FertNet corrects data issues and transforms unstructured network data into alter-attribute and alter-tie datasets that can be handled by a diversity of network analytical approaches. CONTRIBUTION The Social Networks and Fertility Study is a unique resource that allows for a comprehensive investigation of how networks shape fertility behaviour. It provides better estimates of network characteristics than earlier literature based on smaller networks. The R-package FertNet assists researchers in their analyses.</p
Gated networks: an inventory
Gated networks are networks that contain gating connections, in which the
outputs of at least two neurons are multiplied. Initially, gated networks were
used to learn relationships between two input sources, such as pixels from two
images. More recently, they have been applied to learning activity recognition
or multi-modal representations. The aims of this paper are threefold: 1) to
explain the basic computations in gated networks to the non-expert, while
adopting a standpoint that insists on their symmetric nature. 2) to serve as a
quick reference guide to the recent literature, by providing an inventory of
applications of these networks, as well as recent extensions to the basic
architecture. 3) to suggest future research directions and applications.Comment: Unpublished manuscript, 17 page
Collecting large personal networks in a representative sample of Dutch women
In this study we report on our experiences with collecting large personal network data (25 alters) from a representative sample of Dutch women. We made use of GENSI, a recently developed tool for network data collection using interactive visual elements that has been shown to reduce respondent burden. A sample of 758 women between the ages of 18 and 40 were recruited through the LISS-panel; a longitudinal online survey of Dutch people. Respondents were asked to name exactly 25 alters, answer sixteen questions about these alters (name interpreter questions), and assess all 300 alter-alter relations. Nearly all (97%) respondents reported on 25 alters. Non-response was minimal: 92% of respondents had no missing values, and an additional 5% had fewer than 10% missing values. Listing 25 alters took 3.5 ± 2.2 (mean ± SD) minutes, and reporting on the ties between these alters took 3.6 ± 1.3 min. Answering all alter questions took longest with a time of 15.2 ± 5.3 min. The majority of respondents thought the questions were clear and easy to answer, and most enjoyed filling in the survey. Collecting large personal networks can mean a significant burden to respondents, but through the use of visual elements in the survey, it is clear that it can be done within reasonable time, with enjoyment and without much non-response
Policy Search in Continuous Action Domains: an Overview
Continuous action policy search is currently the focus of intensive research,
driven both by the recent success of deep reinforcement learning algorithms and
the emergence of competitors based on evolutionary algorithms. In this paper,
we present a broad survey of policy search methods, providing a unified
perspective on very different approaches, including also Bayesian Optimization
and directed exploration methods. The main message of this overview is in the
relationship between the families of methods, but we also outline some factors
underlying sample efficiency properties of the various approaches.Comment: Accepted in the Neural Networks Journal (Volume 113, May 2019
How might life history theory contribute to life course theory?
In this commentary, we consider how evolutionary biologyâs life history theory (LHT) can be integrated with life course theorizing, to the benefit of both endeavors. We highlight areas where it can add value to existing work in life course theory (LCT), focusing on: how it can add an extra level of explanation, which may be helpful in understanding why individuals focus on their own health and happiness (or why they donât); how insights from comparative work, both across species and across all kinds of human populations, can inform LCT; and how social and biological researchers can come together fruitfully to make progress on the tricky issue of understanding human agenc
Evolutionary perspectives on human height variation
Human height is a highly variable trait, both within and between populations, has a high heritability, and influences the manner in which people behave and are treated in society. Although we know much about human height, this information has rarely been brought together in a comprehensive, systematic fashion. Here, we present a synthetic review of the literature on human height from an explicit evolutionary perspective, addressing its phylogenetic history, development, and environmental and genetic influences on growth and stature. In addition to presenting evidence to suggest the past action of natural selection on human height, we also assess the evidence that natural and sexual selection continues to act on height in contemporary populations. Although there is clear evidence to suggest that selection acts on height, mainly through life-history processes but perhaps also directly, it is also apparent that methodological factors reduce the confidence with which such inferences can be drawn, and there remain surprising gaps in our knowledge. The inability to draw firm conclusions about the adaptiveness of such a highly visible and easily measured trait suggests we should show an appropriate degree of caution when dealing with other human traits in evolutionary perspective.</p
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