1,720,956 research outputs found
Towards simulator enabled offline validation and learning of reinforcement learning agents
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01The student, Pulkit Katdare, accepted the attached license on 2024-07-09 at 13:12.The student, Pulkit Katdare, submitted this Dissertation for approval on 2024-07-09 at 13:13.This Dissertation was approved for publication on 2024-07-11 at 07:14.DSpace SAF Submission Ingestion Package generated from Vireo submission #20969 on 2025-02-04 at 21:25:34Reinforcement learning pertains to a class of algorithms that attempt to learn the right decisions through rigorous self-play. Over time, reinforcement learning has achieved great success across many commercial applications like designing chat-bots, learning to play complex games and robotics. Although reinforcement learning when applied to robotics has demonstrated a lot of promise, it still has a long way to go. This is because, reinforcement learning typically requires the robot to explore diverse actions efficiently to learn the right set of actions. Although exploration is possible in domains like chess, this approach is not really viable in robotics. This is because, robots exploring their actions might be unsafe and lead to robot failures and/or accident. Commercially this can be risky, potentially involving significant sums of money. A common compromise in many reinforcement learning applications is to learn and validate robotic agents extensively in a software based simulation environment of the robot. This will not only allow robot to explore environments, but possibly validate for future safety concerns. After sufficient testing across different possible scenarios in the simulation, the idea is to deploy them to the robot. Although most common, this particular method of implementing robot learning does-not translate well in practice. This is because of the reality gap between the simulator and the real world, which can sometimes lead to drastic changes in the performance from simulation to the real world. The reality gap, which is often called as the sim2real gap, refers to the inability of the simulator to mimic each and every aspect of the robot environment like friction. Another common method to perform reinforcement learning for robots to utilize fixed batch of expert data collected on the robot. Such a method, although promising, does not work well in practice, because the robot does-not generalize well to states not seen in the offline data. In this thesis, we aim to combine different aspects of the above mentioned methods to perform offline validation and learning for robot agents. We note that simulation, although imperfect does allow for exploration and generalization for robotic agents. At the same time, offline data collected from the real world is limited but demonstrates robot's interactions in the real world which is expensive. The key idea of our thesis is to succinctly combine offline data with the simulator to both validate and learn optimal policies for the real world in simulation. Notably we look at performative correction of the simulator to ensure an accurate reflection the robot in the real world. In performative correction, we push for designing techniques which can quantitatively measure the performance of the robot in the real world. What that means is that we utilize rollouts from the simulator as before and re-weight trajectories that resemble the real world performance with a higher weight than the ones which are far off from the real world. To that end, we propose two methods to validate robot's performance in the real world. In chapter 2, we propose a technique that estimates the sim2real gap between the simulator and the real world and corrects for the gap by shaping the reward function. In chapter 3, we refine our correction by looking at the dual formulation of a reinforcement learning problem to provide a min-max optimization which is much harder but more accurate at correcting for simulator performance. In chapter 4, we finally take the first steps in building a better learning algorithm that is able to utlize these validation techniques to perform offline reinforcement learning
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
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
Marginalized Importance Sampling for Off-Environment Policy Evaluation
Get PDFReinforcement Learning (RL) methods are typically sample-inefficient, making
it challenging to train and deploy RL-policies in real world robots. Even a
robust policy trained in simulation requires a real-world deployment to assess
their performance. This paper proposes a new approach to evaluate the
real-world performance of agent policies prior to deploying them in the real
world. Our approach incorporates a simulator along with real-world offline data
to evaluate the performance of any policy using the framework of Marginalized
Importance Sampling (MIS). Existing MIS methods face two challenges: (1) large
density ratios that deviate from a reasonable range and (2) indirect
supervision, where the ratio needs to be inferred indirectly, thus exacerbating
estimation error. Our approach addresses these challenges by introducing the
target policy's occupancy in the simulator as an intermediate variable and
learning the density ratio as the product of two terms that can be learned
separately. The first term is learned with direct supervision and the second
term has a small magnitude, thus making it computationally efficient. We
analyze the sample complexity as well as error propagation of our two
step-procedure. Furthermore, we empirically evaluate our approach on Sim2Sim
environments such as Cartpole, Reacher, and Half-Cheetah. Our results show that
our method generalizes well across a variety of Sim2Sim gap, target policies
and offline data collection policies. We also demonstrate the performance of
our algorithm on a Sim2Real task of validating the performance of a 7 DoF
robotic arm using offline data along with the Gazebo simulator
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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