Adaptive Reinforcement Learning Integrating Exploitation-and Exploration-oriented Learning

1999 ◽  
Vol 3 (6) ◽  
pp. 474-478
Author(s):  
Satoshi Kurihara ◽  
◽  
Rikio Onai ◽  
Toshiharu Sugawara ◽  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Real World ◽  
Large Scale ◽  
Autonomous Systems ◽  
Learning System ◽  
The Internet ◽  
Changing Environment ◽  
The Real ◽  
Exploitation And Exploration

We propose and evaluate an adaptive reinforcement learning system that integrates both exploitation- and exploration-oriented learning (ArLee). Compared to conventional reinforcement learning, ArLee is more robust in a dynamically changing environment and conducts exploration-oriented learning efficiently even in a large-scale environment. It is thus well suited for autonomous systems, for example, software agents and mobile robots, that operate in dynamic, large-scale environments, such as the real world and the Internet. Simulation demonstrates the learning system’s basic effectiveness.

Modern Problems of Teaching Disciplines in the Social and Humanitarian Sphere

2020 ◽  
Author(s):  
Е.Н. Юдина
Keyword(s):  
Real World ◽  
Educational Process ◽  
The Internet ◽  
Sociological Study ◽  
The Real ◽  
Additional Resource ◽  
The Social ◽  
Use Of The Internet

интернет-пространство стало частью реального мира современных студентов. В наши дни особенно актуальна проблема активизации использования интернета как дополнительного ресурса в образовательном процессе. В статье приводятся результаты небольшого социологического исследования, посвященного использованию интернета в преподавании социологических дисциплин. Internet space has become a part of the real world of modern students. The problem of increasing the use of the Internet as an additional resource in the educational process is now particularly topical. The article contains the results of a small sociological study on the use of the Internet in teaching sociological disciplines.

scholarly journals How to train your robot with deep reinforcement learning: lessons we have learned

2021 ◽  
pp. 027836492098785
Author(s):  
Julian Ibarz ◽  
Jie Tan ◽  
Chelsea Finn ◽  
Mrinal Kalakrishnan ◽  
Peter Pastor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Case Studies ◽  
Real World ◽  
Review Article ◽  
The Real ◽  
Complex Skills ◽  
Real World Learning ◽  
Level Sensor ◽  
Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

scholarly journals From Literacy to Information Literacy: Reading for Understanding in the Real World

2021 ◽  
pp. 254-267
Author(s):  
John Royce
Keyword(s):  
Information Literacy ◽  
Real World ◽  
Real Life ◽  
The Internet ◽  
Good Readers ◽  
The Real ◽  
Life Problems ◽  
Complicated Process ◽  
Process Failure ◽  
Vital Component

Good readers evaluate as they go along, open to triggers and alarms which warn that something is not quite right, or that something has not been understood. Evaluation is a vital component of information literacy, a keystone for reading with understanding. It is also a complex, complicated process. Failure to evaluate well may prove expensive. The nature and amount of information on the Internet make evaluation skills ever more necessary. Looking at research studies in reading and in evaluation, real-life problems are suggested for teaching, modelling and discussion, to bring greater awareness to good, and to less good, readers.

scholarly journals Curriculum Learning for Natural Answer Generation

2018 ◽  
Author(s):  
Cao Liu ◽  
Shizhu He ◽  
Kang Liu ◽  
Jun Zhao
Keyword(s):  
Full Advantage ◽  
Real World ◽  
Large Scale ◽  
Question Answering ◽  
Generative Models ◽  
The Internet ◽  
Basic Model ◽  
The Arts ◽  
Learning Data

By reason of being able to obtain natural language responses, natural answers are more favored in real-world Question Answering (QA) systems. Generative models learn to automatically generate natural answers from large-scale question answer pairs (QA-pairs). However, they are suffering from the uncontrollable and uneven quality of QA-pairs crawled from the Internet. To address this problem, we propose a curriculum learning based framework for natural answer generation (CL-NAG), which is able to take full advantage of the valuable learning data from a noisy and uneven-quality corpus. Specifically, we employ two practical measures to automatically measure the quality (complexity) of QA-pairs. Based on the measurements, CL-NAG firstly utilizes simple and low-quality QA-pairs to learn a basic model, and then gradually learns to produce better answers with richer contents and more complete syntaxes based on more complex and higher-quality QA-pairs. In this way, all valuable information in the noisy and uneven-quality corpus could be fully exploited. Experiments demonstrate that CL-NAG outperforms the state-of-the-arts, which increases 6.8% and 8.7% in the accuracy for simple and complex questions, respectively.

Lying on the Internet

2012 ◽  
pp. 944-959 ◽  
Author(s):  
Stepan Konecny
Keyword(s):  
Mass Media ◽  
Real World ◽  
Electronic Communication ◽  
Computer Mediated Communication ◽  
Linguistic Analysis ◽  
The Internet ◽  
Mediated Communication ◽  
The Real ◽  
Computer Mediated ◽  
Individual Effects

Mass media often presents a warped image of the Internet as an unreliable environment in which nobody can be trusted. In this entry, the authors describe lying on the Internet both in the context of lying in the real world and with respect to the special properties of computer-mediated communication (CMC). They deal with the most frequent motives for lying online, such as increasing one’s attractiveness or experimenting with identities. They also take into account the various environments of the Internet and their individual effects on various properties of lying. The current methods for detecting lies and the potential for future computer-linguistic analysis of hints for lying in electronic communication are also considered.

Technology-Supported Tourist Guiding in Aviation Tourism

2020 ◽  
pp. 257-267
Author(s):  
Azizul Hassan
Keyword(s):  
Augmented Reality ◽  
Diffusion Of Innovation ◽  
Real World ◽  
Perceptual Information ◽  
The Internet ◽  
Diffusion Of Innovation Theory ◽  
The Real ◽  
National Flag ◽  
Interactive Experience ◽  
World Environment

Augmented reality (AR) offers an interactive experience of the real-world environment when an object of the real-world is augmented by computer-generated perceptual information and relevant artefacts. This is a conceptual chapter based on the review of available literature. Also, resources on the internet have also been accessed and reviewed. On the context of the Diffusion of Innovation theory, this research aims to outline AR guiding for in an airport used for tourist aviation. Biman Bangladesh Airlines, the national flag carrier of the country, is the example where this study also explains the possible challenges and benefits that AR guiding facilities can possibly have. This research outlines two specific areas of management and marketing issues are analysis on the way to implement such guiding. Findings show that from the understanding of the Diffusion of Innovation, AR guiding in these days is adopted by an ‘Early Majority' who are followers and engages in reading those reviews given by the previous adopters of new services or products.

scholarly journals City brain: practice of large‐scale artificial intelligence in the real world

2019 ◽  
Vol 1 (1) ◽  
pp. 28-37 ◽  
Author(s):  
Jianfeng Zhang ◽  
Xian‐Sheng Hua ◽  
Jianqiang Huang ◽  
Xu Shen ◽  
Jingyuan Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Large Scale ◽  
The Real

The ontogeny of a mammalian cognitive map in the real world

Science ◽  
2020 ◽  
Vol 369 (6500) ◽  
pp. 194-197 ◽  
Author(s):  
Lee Harten ◽  
Amitay Katz ◽  
Aya Goldshtein ◽  
Michal Handel ◽  
Yossi Yovel
Keyword(s):  
Real World ◽  
Large Scale ◽  
Cognitive Maps ◽  
Cognitive Map ◽  
Interindividual Differences ◽  
The Real ◽  
Fruit Bat ◽  
Wild Fruit ◽  
In The Wild

How animals navigate over large-scale environments remains a riddle. Specifically, it is debated whether animals have cognitive maps. The hallmark of map-based navigation is the ability to perform shortcuts, i.e., to move in direct but novel routes. When tracking an animal in the wild, it is extremely difficult to determine whether a movement is truly novel because the animal’s past movement is unknown. We overcame this difficulty by continuously tracking wild fruit bat pups from their very first flight outdoors and over the first months of their lives. Bats performed truly original shortcuts, supporting the hypothesis that they can perform large-scale map-based navigation. We documented how young pups developed their visual-based map, exemplifying the importance of exploration and demonstrating interindividual differences.

scholarly journals Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 6194-6201
Author(s):  
Jing Wang ◽  
Weiqing Min ◽  
Sujuan Hou ◽  
Shengnan Ma ◽  
Yuanjie Zheng ◽  
...  
Keyword(s):  
Image Recognition ◽  
Real World ◽  
Large Scale ◽  
Data Augmentation ◽  
Ground Truth ◽  
Classification Task ◽  
The Real ◽  
Product Recommendation ◽  
Contextual Advertising ◽  
Benchmark Datasets

Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have larger variety in logo appearance and more complexity in their background. Therefore, recognizing the logo from images is challenging. To support efforts towards scalable logo classification task, we have curated a dataset, Logo-2K+, a new large-scale publicly available real-world logo dataset with 2,341 categories and 167,140 images. Compared with existing popular logo datasets, such as FlickrLogos-32 and LOGO-Net, Logo-2K+ has more comprehensive coverage of logo categories and larger quantity of logo images. Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification. DRNA-Net consists of four sub-networks: the navigator sub-network first selected informative logo-relevant regions guided by the teacher sub-network, which can evaluate its confidence belonging to the ground-truth logo class. The data augmentation sub-network then augments the selected regions via both region cropping and region dropping. Finally, the scrutinizer sub-network fuses features from augmented regions and the whole image for logo classification. Comprehensive experiments on Logo-2K+ and other three existing benchmark datasets demonstrate the effectiveness of proposed method. Logo-2K+ and the proposed strong baseline DRNA-Net are expected to further the development of scalable logo image recognition, and the Logo-2K+ dataset can be found at https://github.com/msn199959/Logo-2k-plus-Dataset.

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