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Author: Takayuki Osa Publisher: ISBN: 9781680834109 Category : Technology & Engineering Languages : en Pages : 194
Book Description
Familiarizes machine learning experts with imitation learning, statistical supervised learning theory, and reinforcement learning. It also roboticists and experts in applied artificial intelligence with a broader appreciation for the frameworks and tools available for imitation learning.
Author: Shai Shalev-Shwartz Publisher: Cambridge University Press ISBN: 1107057132 Category : Computers Languages : en Pages : 415
Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Faraz Torabi Publisher: ISBN: Category : Languages : en Pages : 420
Book Description
Advances in robotics have resulted in increases both in the availability of robots and also their complexity—a situation that necessitates automating both the execution and acquisition of robot behaviors. For this purpose, multiple machine learning frameworks have been proposed, including reinforcement learning and imitation learning. Imitation learning in particular has the advantage of not requiring a human engineer to attempt the difficult process of cost function design necessary in reinforcement learning. Moreover, compared to reinforcement learning, imitation learning typically requires less exploration time before an acceptable behavior is learned. These advantages exist because, in the framework of imitation learning, a learning agent has access to an expert agent that demonstrates how a task should be performed. Broadly speaking, this framework has a limiting constraint in that it requires the learner to have access not only to the states (e.g., observable quantities such as spatial location) of the expert, but also to its actions (e.g., internal control signals such as motor commands). This constraint is limiting in the sense that it prevents the agent from taking advantage of potentially rich demonstration resources that do not contain action information, e.g., YouTube videos. To alleviate this restriction, Imitation Learning from Observation (IfO) has recently been introduced as an imitation learning framework that explicitly seeks to learn behaviors by observing state-only expert demonstrations. The IfO problem has two main components: (1) perception of the demonstrations, and (2) learning a control policy. This thesis focuses primarily on the second component, and introduces multiple algorithms to solve the control aspect of the problem. Each of the proposed algorithms has certain advantages and disadvantages over the others in terms of performance, stability and sample complexity. Moreover, some of the algorithms are model-based (i.e., a model of the dynamics of the environment is learned in the imitation learning process), and some are model-free. In general, model-based algorithms are more sample-efficient, whereas model-free algorithms are known for their performance. Though the focus of this thesis is on the control aspect of IfO, two algorithms are introduced that do integrate a perception module into one of the control algorithms. By doing so, the adaptability of that control algorithm to the general IfO problem is shown. The work in this thesis is evaluated primarily in simulation, though in some cases experiments were carried out using real-world robots as well. The performance of the proposed algorithms is compared against well-known baselines and it is shown that they outperform the baselines in most cases
Author: Shai Avidan Publisher: Springer Nature ISBN: 3031198425 Category : Computers Languages : en Pages : 785
Book Description
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author: Steven M. LaValle Publisher: Springer Nature ISBN: 3030667235 Category : Technology & Engineering Languages : en Pages : 581
Book Description
This proceedings book helps bring insights from this array of technical sub-topics together, as advanced robot algorithms draw on the combined expertise of many fields—including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. Intelligent robots and autonomous systems depend on algorithms that efficiently realize functionalities ranging from perception to decision making, from motion planning to control. The works collected in this SPAR book represent the state of the art in algorithmic robotics. They originate from papers accepted to the 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR), traditionally a biannual, single-track meeting of leading researchers in the field of robotics. WAFR has always served as a premiere venue for the publication of some of robotics’ most important, fundamental, and lasting algorithmic contributions, ensuring the rapid circulation of new ideas. Though an in-person meeting was planned for June 15–17, 2020, in Oulu, Finland, the event ended up being canceled owing to the infeasibility of international travel during the global COVID-19 crisis.
Author: Matthew F. Dixon Publisher: Springer Nature ISBN: 3030410684 Category : Business & Economics Languages : en Pages : 565
Book Description
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Author: Eugene A. Feinberg Publisher: Springer Science & Business Media ISBN: 1461508053 Category : Business & Economics Languages : en Pages : 560
Book Description
Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. The goal is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation.
Author: Vladimir Vovk Publisher: Springer Science & Business Media ISBN: 9780387001524 Category : Computers Languages : en Pages : 344
Book Description
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.
Author: Marco Morales Publisher: Springer Nature ISBN: 3030440516 Category : Technology & Engineering Languages : en Pages : 959
Book Description
This book gathers the outcomes of the thirteenth Workshop on the Algorithmic Foundations of Robotics (WAFR), the premier event for showcasing cutting-edge research on algorithmic robotics. The latest WAFR, held at Universidad Politécnica de Yucatán in Mérida, México on December 9–11, 2018, continued this tradition. This book contains fifty-four papers presented at WAFR, which highlight the latest research on fundamental algorithmic robotics (e.g., planning, learning, navigation, control, manipulation, optimality, completeness, and complexity) demonstrated through several applications involving multi-robot systems, perception, and contact manipulation. Addressing a diverse range of topics in papers prepared by expert contributors, the book reflects the state of the art and outlines future directions in the field of algorithmic robotics.
Author: Takayuki Osa
Author: Takayuki Osa
Author: Takayuki Osa
Author: Shai Shalev-Shwartz
Author: Faraz Torabi
Author: Shai Avidan
Author: Steven M. LaValle
Author: Matthew F. Dixon
Author: Eugene A. Feinberg
Author: Vladimir Vovk
Author: Marco Morales