1,721,004 research outputs found
LEARNING TRANSFERABLE META-POLICIES FOR HIERARCHICAL TASK DECOMPOSITION AND PLANNING COMPOSITION
Get PDFIn real world scenarios where situated agents are faced with dynamic, high-dimensional, partially observable environments with action and reward uncertainty, the traditional states space Reinforcement Learning (RL) becomes easily prohibitively large for policy learning. In such scenarios, addressing the curse of dimensionality and eventual transfer to closely related tasks is one of the principal challenges and motivations for Hierarchical Reinforcement Learning (HRL). The prime appeal of hierarchal and particularly recursive approaches is in effective factored state, transition and reward representations which abstract out aspects that are not relevant to subtasks and allow potential transfer of skills which represent solutions to potential task subspaces. With the advent of deep learning techniques, a range of techniques for representation learning have become available for a range of problems, mostly in supervised learning applications, however, relatively little has been applied in the context of hierarchical Reinforcement Learning where different time scales are important and where limited access to large training data sets and reduced feedback has made learning on these structures difficult. Moreover, the addition of partial observability and the corresponding need to encode memory through recurrent connections further increase this complexity and very limited work in this direction exists. This dissertation investigates the use of recurrent deep learning structures to automatically learn hierarchical state and policy structures without the need for supervised data in the context of Reinforcement Learning problems. In particular, it proposes and evaluates two novel network architectures, one based on Conditional Restricted Boltzmann Machines (CRBM) and one using a Multidimensional Multidirectional Multiscale LSTM network. Experiments using a very sparsely observable version of the common taxi domain problem show the potential of the architectures and illustrate its ability to build hierarchical, reusable representations both in terms of state representations and learned policy actions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Hierarchical Reinforcement Learning Using Automatic Task Decomposition And Exploration Shaping
No full textReinforcement learning agents situated in real world environments have to be able to address a number of challenges in order to succeed at accomplishing a wide range of tasks over their lifetime. Among these, such systems have to be able to extract control knowledge from already learned tasks and apply them to subsequent ones in order to allow the agent to accomplish the new task faster and to accelerate the learning of an optimal policy. To address skill reuse and skill transfer, a number of approaches using hierarchical state and action spaces have been introduced recently which build on the idea of transferring the previously learned policies and representations to model and control the new task. However, while such transfer of skills can significantly improve learning times, it also poses the risk of behavior proliferation where the increasing set of available reusable actions makes it incrementally more difficult to determine a strategy for a new task. To address this issue, it is important for the agent to have the capability to analyze new tasks and to have a means of predicting the utility of an action or skill in a new context prior to learning a policy for the task. The former here implies an ability to decompose the new task into known subtasks while the latter implies the availability of an informed exploration policy used to find the new goal and to more efficiently learn a corresponding policy. This thesis presents a novel approach for learning task decomposition by learning to predict the utility of subgoals and subgoal types in the context of the new task, as well as for exploration shaping by predicting the likelihood with which each available action is useful in the given task context. To achieve this, the approach presented here uses past learning experiences to acquire set of utility functions that encode relevant knowledge about useful subgoals and skills and applies them to shape the search for the optimal policy for the new task. Acceleration is achieved by focusing the search on contextually identifiable subgoals and actions/skills that have been learned to be valuable in the context of optimal policies in the previously encountered worlds. Performance increase is achieved here both in terms of the time required to reach the task\u27s goal the first time and time required to learn an optimal policy, which is demonstrated in the context of navigation and manipulation tasks in a grid world domain
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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