1,720,955 research outputs found
Learning a Belief Representation for Delayed Reinforcement Learning
No full textThis paper considers sequential decision-making problems where the interactions between an agent and its environment are affected by delays. Delays may be present in the state observation, in the action execution, or in the reward collection. We consider the delayed Markov Decision Process (MDP) framework both in the case of deterministic and stochastic delays. Given the hardness of the delayed MDP problem, we use a heuristic approach to design an algorithm that uses the belief over the current unobserved state to select its action. We design a self-attention prediction module which, given the last observed state and the following sequence of actions, estimates the beliefs over the following states. This algorithm is able to deal with deterministic delays and could potentially be extended to stochastic delays. We empirically evaluate the effectiveness of the proposed approach in both deterministic and stochastic control problems affected by deterministic delays
Addressing Non-Stationarity in FX Trading with Online Model Selection of Offline RL Experts
No full textReinforcement learning has proven to be successful in obtaining profitable trading policies; however, the effectiveness of such strategies is strongly conditioned to market stationarity. This hypothesis is challenged by the regime switches frequently experienced by practitioners; thus, when many models are available, validation may become a difficult task. We propose to overcome the issue by explicitly modeling the trading task as a non-stationary reinforcement learning problem. Nevertheless, state-of-the-art RL algorithms for this setting usually require task distribution or dynamics to be predictable, an assumption that can hardly be true in the financial framework. In this work, we propose, instead, a method for the dynamic selection of the best RL agent which is only driven by profit performance. Our modular two-layer approach allows choosing the best strategy among a set of RL models through an online-learning algorithm. While we could select any combination of algorithms in principle, our solution employs two state-of-the-art algorithms: Fitted Q-Iteration (FQI) for the RL layer and Optimistic Adapt ML-Prod (OAMP) for the online learning one. The proposed approach is tested on two simulated FX trading tasks, using actual historical data for the AUS/USD and GBP/USD currency pairs
Foreign exchange trading: A risk-averse batch reinforcement learning approach
Full text linkAutomated Trading Systems' impact on financial markets is ever growing, particularly on the intraday Foreign Exchange market. Historically, the FX trading systems are based on advanced statistical methods and technical analysis able to extract trading signals from financial data. In this work, we explore how to find a trading strategy via Reinforcement Learning by means of a state-of-the-art batch algorithm, Fitted Q-Iteration. Furthermore, we include a Multi-Objective formulation of the problem to keep the risk of noisy profits under control. We show that the algorithm is able to detect favorable temporal patterns, which are used by the agent to maximize the return. Finally, we show that as risk aversion increases, the resulting policies become smoother, as the portfolio positions are held for longer periods
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“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
Full text linkWe 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
Full text linkWe 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
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