Conferences related to Online Learning Systems

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2014 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2014 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2013 IEEE Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2013 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2012 IEEE Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2012 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2011 Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE) Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2011 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2010 IEEE Frontiers in Education Conference (FIE)

    (FIE) Conference is a major international conference devoted to improvements in computer science, engineering, and technology (CSET) education. FIE 2008 continues a long tradition of disseminating educational research results and innovative practices in CSET education. It is an ideal forum for sharing ideas, learning about developments in CSET education, and interacting with colleagues.

  • 2009 IEEE Frontiers in Education Conference (FIE)

    FIE is a major international conference devoted to improvements in computer science, engineering and technology (CSET) education. FIE continues a loong tradition of disseminating educational research results and innovative practices in CSET education. It is an ideal forum for sharing ideas, learning about developments in CSET education, and interacting with colleagues.

  • 2008 IEEE Frontiers in Education Conference (FIE)

  • 2007 IEEE Frontiers in Education Conference (FIE)

    Globalization has dramatically changed engineering. Global engineering teams design products for global markets. Knowledge has no borders in a world where information flow is digitalized and sent worldwide in seconds. A core requirement of engineering globalization is an understanding of how the different cultures of the global marketplace shape product development, mult-national engineering teams, and consumer expectations. Engineering education must address this issue.

  • 2006 IEEE Frontiers in Education Conference (FIE)

  • 2005 IEEE Frontiers in Education Conference (FIE)


2013 8th International Forum on Strategic Technology (IFOST)

Strategic technologies including advanced materials, applied engineering sciences, information technologies, mechanical engineering, and so on.

  • 2012 7th International Forum on Strategic Technology (IFOST)

    International Forum on Strategic Technology is an annual academic conference and technical forum for researchers, engineers, industry representatives and policy planners.

  • 2011 6th International Forum on Strategic Technology (IFOST)

    1. Advanced Materials / Nano Technology 2. Renewable Energy / Smart Grid 3. Information Technology 4. E-vehicle / Green Car 5. Mechatronics 6. Others

  • 2010 International Forum on Strategic Technology (IFOST)

    a. e-Vehicle / Green Car b. New Materials c. Renewable Energy d. Smart Grid e. Bio-/Chemio-/Nano- Technology f. Other Strategic Technology

  • 2008 3rd International Forum on Strategic Technology (IFOST)

    New materials and technologies Nanotechnologies Information technologies Mechatronics and Automation Power engineering and resource-saving Environmental protection and conservancy

  • 2007 International Forum on Strategic Technology (IFOST)

    The International Forum organized under the main topic Power Engineering - Ecology will be carried out through the following sections: 1. Power Engineering 2. Mining Production and Metallurgy 3. Civil Engineering and Geo-Engineering 4. Information and Communication Technology 5. Ecosphere and Industrial Ecology


2013 IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

GEFS2013 will provide an opportunity to meet researchers working on the topic, make new contacts and exchange ideas. The GEFS series of workshops are an important part of the activities of the Evolutionary Fuzzy Systems Task Force of the Fuzzy System Technical Committee (IEEE Computational Intelligence Society).

  • 2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)

    Genetic and evolutionary fuzzy systems meld the approximate reasoning method of fuzzy systems with the adaptation capabilities of evolutionary algorithms. The objective of GEFS2010 is to facilitate the promotion of novel problems, research, results, and future directions in the latter growing area.

  • 2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)

    One of the most prominent approaches to hybridize fuzzy systems with learning and adaptation methods has resulted in the emergence of genetic and evolving fuzzy systems, which combine the approximate reasoning method of fuzzy systems with the adaptation capabilities of evolutionary algorithms. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans.


2011 11th International Conference on Hybrid Intelligent Systems (HIS 2011)

The objectives of HIS 2011 are: to increase the awareness of the research community of the broad spectrum of hybrid techniques, to bring together AI researchers from around the world to present their cutting-edge results, to discuss the current trends in HIS research, to develop a collective vision of future opportunities, to establish international collaborative opportunities, and as a result to advance the state of the art of the field.

  • 2010 10th International Conference on Hybrid Intelligent Systems (HIS 2010)

    Hybridization of intelligent systems is a promising research field of computational intelligence focusing on synergistic combinations of multiple approaches to develop the next generation of intelligent systems. A fundamental stimulus to the investigations of Hybrid Intelligent Systems (HIS) is the awareness that combined approaches will be necessary if the remaining tough problems in Artificial Intelligence are to be solved. Join the feast of Hybrid Computational Intelligence researchers! Neural c

  • 2009 9th International Conference on Hybrid Intelligent Systems (HIS 2009)

    Hybridization of intelligent systems is a promising research field of computational intelligence focusing on synergistic combinations of multiple approaches to develop the next generation of intelligent systems. The objectives of HIS 2009 are to bring world-wide AI researchers together to present their cutting-edge results, to develop a collective vision of future opportunities, to establish international collaborative opportunities, and as a result to advance the state of the art in AI fields.

  • 2008 8th International Conference on Hybrid Intelligent Systems (HIS 2008)

    Hybridization provides the leverage to deal with complexity and performance challenges imposed on intelligent systems and their physical embodiment. The conference goals are to provide a forum for advanced methods from neural computing, machine learning, fuzzy logic, evolutionary algorithms and related techniques and their combination to efficient systems.

  • 2007 7th International Conference on Hybrid Intelligent Systems (HIS 2007)

    Hybridization provides the leverage to deal with complexity and performance challenges imposed on intelligent systems and their physical embodiment. The conference goals are to provide a forum for advanced methods from neural computing, machine learning, fuzzy logic, evolutionary algorithms, and related techniques and their combination to efficient systems. In particular, approaches to intelligent system design as well as dynamic aspects of system evolution is a major focus of the conference.



Periodicals related to Online Learning Systems

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Education, IEEE Transactions on

Educational methods, technology, and programs; history of technology; impact of evolving research on education.


Intelligent Transportation Systems, IEEE Transactions on

The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical ...


Learning Technologies, IEEE Transactions on

The IEEE Transactions on Learning Technologies publishes archival research papers and critical survey papers on technology advances in online learning systems; intelligent tutors; educational software applications and games; simulation systems for education and training; collaborative learning tools, devices and interfaces for learning; interactive techniques for learning; tools for formative and summative assessment; ontologies for learning systems; standards and web services ...


Systems Journal, IEEE

This publication provides a systems-level, focused forum for application-oriented manuscripts that address complex systems and system-of-systems of national and global significance.


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...




Xplore Articles related to Online Learning Systems

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Leveraging Biometrics for User Authentication in Online Learning: A Systems Perspective

Assad Moini; Azad M. Madni IEEE Systems Journal, 2009

With the rapid proliferation of online learning, students are increasingly demanding easy and flexible access to learning content at a time and location of their choosing. In these environments, remote users connecting via the public Internet or other unsecure networks must be authenticated prior to being granted access to sensitive content such as tests or personal/private records. Today, the overwhelming ...


Using Massive Processing and Mining for Modelling and Decision Making in Online Learning Systems

Fatos Xhafa; Santi Caballe; Nik Bessis; Angel A. Juan; Leonard Barolli; Rozeta Miho Emerging Intelligent Data and Web Technologies (EIDWT), 2011 International Conference on, 2011

Online Learning and Virtual Campuses have become commonplace paradigms for distance teaching and learning. Unlike face to face teaching and learning methods in which teachers and managers can take decisions based on information from everyday classroom activities, decision making in online learning becomes more complex due to the online setting. Teachers need to get information from the online learning system ...


Towards the verification and validation of online learning systems: general framework and applications

A. Mili; GuangJie Jiang; B. Cukic; Yan Liu; R. B. Ayed System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on, 2004

Online adaptive systems cannot be certified using traditional testing and proving methods, because these methods rely on assumptions that do not hold for such systems. In this paper, we discuss a framework for reasoning about online adaptive systems, and see how this framework can be used to perform the verification of these systems. In addition to the framework, we present ...


Adaptive word processor based on Morse code

Nadine Akkari; Ghada Alfattni; Hana Alghamdi; Mona Alzahrani; Manal Alkhammash Web and Open Access to Learning (ICWOAL), 2014 International Conference on, 2014

Adaptive open learning technology provides adaptive methods of interacting with the technology used in open learning. Since most of online learning systems are computer-based, adapting the keyboard for people with special needs will be of great effectiveness. This article presents an adaptive keyboard technology based on Morse code that will enable users with physical disability or functional limitations to access ...


MSys: An activities tracking tool for E-learning systems

Christiane Meiler Baptista; Regina Melo Silveira; Wilson Vicente Ruggiero 2008 38th Annual Frontiers in Education Conference, 2008

E-learning content creation is not an easy task, but its evaluation is even more complex. In order to evaluate if the content is adequate to the studentspsila needs, it would be helpful to know how the student assimilated the learning content, how he/she reacted to it and the period of time spent on the learning object. Besides, considering the different ...


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Educational Resources on Online Learning Systems

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eLearning

Leveraging Biometrics for User Authentication in Online Learning: A Systems Perspective

Assad Moini; Azad M. Madni IEEE Systems Journal, 2009

With the rapid proliferation of online learning, students are increasingly demanding easy and flexible access to learning content at a time and location of their choosing. In these environments, remote users connecting via the public Internet or other unsecure networks must be authenticated prior to being granted access to sensitive content such as tests or personal/private records. Today, the overwhelming ...


Using Massive Processing and Mining for Modelling and Decision Making in Online Learning Systems

Fatos Xhafa; Santi Caballe; Nik Bessis; Angel A. Juan; Leonard Barolli; Rozeta Miho Emerging Intelligent Data and Web Technologies (EIDWT), 2011 International Conference on, 2011

Online Learning and Virtual Campuses have become commonplace paradigms for distance teaching and learning. Unlike face to face teaching and learning methods in which teachers and managers can take decisions based on information from everyday classroom activities, decision making in online learning becomes more complex due to the online setting. Teachers need to get information from the online learning system ...


Towards the verification and validation of online learning systems: general framework and applications

A. Mili; GuangJie Jiang; B. Cukic; Yan Liu; R. B. Ayed System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on, 2004

Online adaptive systems cannot be certified using traditional testing and proving methods, because these methods rely on assumptions that do not hold for such systems. In this paper, we discuss a framework for reasoning about online adaptive systems, and see how this framework can be used to perform the verification of these systems. In addition to the framework, we present ...


Adaptive word processor based on Morse code

Nadine Akkari; Ghada Alfattni; Hana Alghamdi; Mona Alzahrani; Manal Alkhammash Web and Open Access to Learning (ICWOAL), 2014 International Conference on, 2014

Adaptive open learning technology provides adaptive methods of interacting with the technology used in open learning. Since most of online learning systems are computer-based, adapting the keyboard for people with special needs will be of great effectiveness. This article presents an adaptive keyboard technology based on Morse code that will enable users with physical disability or functional limitations to access ...


MSys: An activities tracking tool for E-learning systems

Christiane Meiler Baptista; Regina Melo Silveira; Wilson Vicente Ruggiero 2008 38th Annual Frontiers in Education Conference, 2008

E-learning content creation is not an easy task, but its evaluation is even more complex. In order to evaluate if the content is adequate to the studentspsila needs, it would be helpful to know how the student assimilated the learning content, how he/she reacted to it and the period of time spent on the learning object. Besides, considering the different ...


More eLearning Resources

IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Online Learning Systems"

IEEE-USA E-Books

  • Index

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Index

    The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. _ Introduction to Machine Learnin_g is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of _Introduction to Machin Learning_ reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

  • Spatially-Adaptive Learning Rates for Online Incremental SLAM

    Several recent algorithms have formulated the SLAM problem in terms of non- linear pose graph optimization. These algorithms are attractive because they offer lower computational and memory costs than the traditional Extended Kalman Filter (EKF), while simultaneously avoiding the linearization error problems that affect EKFs. In this paper, we present a new non-linear SLAM algorithm that allows incremental optimization of pose graphs, i.e., allows new poses and constraints to be added without requiring the solution to be recomputed from scratch. Our approach builds upon an existing batch algorithm that combines stochastic gradient descent and an incremental state representation. We develop an incremental algorithm by adding a spatially- adaptive learning rate, and a technique for reducing computational requirements by restricting optimization to only the most volatile portions of the graph. We demonstrate our algorithms on real datasets, and compare against other online algorithms.

  • Back Matter

    The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. _ Introduction to Machine Learnin_g is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of _Introduction to Machin Learning_ reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

  • Online Stochastic Routing

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • No title

    Human computation is a new and evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. With the growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of people via the Web to perform complex computation. There are various genres of human computation applications that exist today. Games with a purpose (e.g., the ESP Game) specifically target online gamers who generate useful data (e.g., image tags) while playing an enjoyable game. Crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) are human computation systems that coordinate workers to perform tasks in exchange for monetary rewards. In identity verification tasks, users perform computation in order to gain access to some online content; an example is reCAPTCHA, which leverages millions of users who solve CAPTCHAs every day to correct words in books that ptical character recognition (OCR) programs fail to recognize with certainty. This book is aimed at achieving four goals: (1) defining human computation as a research area; (2) providing a comprehensive review of existing work; (3) drawing connections to a wide variety of disciplines, including AI, Machine Learning, HCI, Mechanism/Market Design and Psychology, and capturing their unique perspectives on the core research questions in human computation; and (4) suggesting promising research directions for the future. Table of Contents: Introduction / Human Computation Algorithms / Aggregating Outputs / Task Routing / Understanding Workers and Requesters / The Art of Asking Questions / The Future of Human Computation

  • E-Graphs: Bootstrapping Planning with Experience Graphs

    Human environments possess a significant amount of underlying structure that is under-utilized in motion planning and mobile manipulation. In domestic environments for example, walls and shelves are static, large objects such as furniture and kitchen appliances most of the time do not move and do not change, and objects are typically placed on a limited number of support surfaces such as tables, countertops or shelves. Motion planning for robots operating in such environments should be able to exploit this structure to improve its performance with each execution of a task. In this paper, we develop an online motion planning approach which learns from its planning episodes (experiences) a graph, an Experience Graph. This graph represents the underlying connectivity of the space required for the execution of the mundane tasks performed by the robot. The planner uses the Experience graph to accelerate its planning efforts whenever possible. On the theoretical side, we show that planning with Experience graphs is complete and provides bounds on sub-optimality with respect to the graph that represents the original planning problem. On the experimental side, we show in simulations and on a physical robot that our approach is particularly suitable for higher-dimensional motion planning tasks such as planning for single-arm manipulation and two armed mobile manipulation. The approach provides significant speedups over planning from scratch and generates predictable motion plans: motions planned from start positions that are close to each other to goal positions that are also close to each other tend to be similar. In addition, we show how the Experience graphs can incorporate solutions from other approaches such as human demonstrations, providing an easy way of bootstrapping motion planning for complex tasks.

  • Online Stochastic Reservations

    Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.

  • Artificial Intelligence, Document Processing, and HyperText

    Text, ConText, and HyperText presents recent developments in three related and important areas of technical communication: the design of effective documentation; the impact of new technology and research on technical writing; and the training and management of technical writers.The contributors are all authorities drawn from universities and industry who are active in defining and analyzing the role of computing in technical documentation and the role of documentation in the development of computing technology. This first synthesis of their diverse but related research provides a unique conceptualization of the field of computers and writing and documentation.The book first examines techniques for writing online documentation and the value of usability testing. It presents new research into the impact of human factors in screen design and designing online help, and looks at the impact of desktop publishing on documentation, and at visual literacy and graphic design.Artificial intelligence and documentation processing are then addressed with discussion of data acquisition, automated formatting in expert systems, and document databases; the uses of HyperText in documentation; and the future of technical writing in this new environment.Text, ConText, and HyperText concludes by examining the training and management of documentation groups: how they "learn to write" in industry, management of large-scale documentation projects and their effect on product development; and the "two cultures" of engineering and documentation.Edward Barrett is a Lecturer in the Writing Program at MIT. Text, ConText, and HyperText is included in the Information Systems series, edited by Michael Lesk.

  • Natural Actor-Critic for Road Traffic Optimisation

    Current road-traffic optimisation practice around the world is a combination of hand tuned policies with a small degree of automatic adaption. Even state- ofthe- art research controllers need good models of the road traffic, which cannot be obtained directly from existing sensors. We use a policy-gradient reinforcement learning approach to directly optimise the traffic signals, mapping currently deployed sensor observations to control signals. Our trained controllers are (theoretically) compatible with the traffic system used in Sydney and many other cities around the world. We apply two policy-gradient methods: (1) the recent natural actor-critic algorithm, and (2) a vanilla policy-gradient algorithm for comparison. Along the way we extend natural- actor critic approaches to work for distributed and online infinite-horizon problems



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