Exploration Exploitation Dilemma In Reinforcement Learning Under Various Form Of Prior Knowledge

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Reinforcement Learning, second edition

Author : Richard S. Sutton
Publisher : MIT Press
ISBN 13 : 0262352702
Total Pages : 549 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton

Download or read book Reinforcement Learning, second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Agents and Artificial Intelligence

Author : Joaquim Filipe
Publisher : Springer
ISBN 13 : 3642369073
Total Pages : 458 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Agents and Artificial Intelligence by : Joaquim Filipe

Download or read book Agents and Artificial Intelligence written by Joaquim Filipe and published by Springer. This book was released on 2013-04-10 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Agents and Artificial Intelligence, ICAART 2012, held in Vilamoura, Portugal, in February 2012. The 28 revised full papers presented together with one invited paper were carefully reviewed and selected from 292 submissions. The papers are organized in two topical sections on artificial intelligence and on agents.

Mathematics for Machine Learning

Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
ISBN 13 : 1108569323
Total Pages : 392 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Intrinsic motivations and open-ended development in animals, humans, and robots

Author : Gianluca Baldassarre
Publisher : Frontiers E-books
ISBN 13 : 2889193721
Total Pages : 351 pages
Book Rating : 4.8/5 (891 download)

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Book Synopsis Intrinsic motivations and open-ended development in animals, humans, and robots by : Gianluca Baldassarre

Download or read book Intrinsic motivations and open-ended development in animals, humans, and robots written by Gianluca Baldassarre and published by Frontiers E-books. This book was released on 2015-02-10 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this Research Topic for Frontiers in Psychology under the section of Cognitive Science and Frontiers in Neurorobotics is to present state-of-the-art research, whether theoretical, empirical, or computational investigations, on open-ended development driven by intrinsic motivations. The topic will address questions such as: How do motivations drive learning? How are complex skills built up from a foundation of simpler competencies? What are the neural and computational bases for intrinsically motivated learning? What is the contribution of intrinsic motivations to wider cognition? Autonomous development and lifelong open-ended learning are hallmarks of intelligence. Higher mammals, and especially humans, engage in activities that do not appear to directly serve the goals of survival, reproduction, or material advantage. Rather, a large part of their activity is intrinsically motivated - behavior driven by curiosity, play, interest in novel stimuli and surprising events, autonomous goal-setting, and the pleasure of acquiring new competencies. This allows the cumulative acquisition of knowledge and skills that can later be used to accomplish fitness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans artistic creativity, scientific discovery, and subjective well-being owe much to them. The study of intrinsically motivated behavior has a long history in psychological and ethological research, which is now being reinvigorated by perspectives from neuroscience, artificial intelligence and computer science. For example, recent neuroscientific research is discovering how neuromodulators like dopamine and noradrenaline relate not only to extrinsic rewards but also to novel and surprising events, how brain areas such as the superior colliculus and the hippocampus are involved in the perception and processing of events, novel stimuli, and novel associations of stimuli, and how violations of predictions and expectations influence learning and motivation. Computational approaches are characterizing the space of possible reinforcement learning algorithms and their augmentation by intrinsic reinforcements of different kinds. Research in robotics and machine learning is yielding systems with increasing autonomy and capacity for self-improvement: artificial systems with motivations that are similar to those of real organisms and support prolonged autonomous learning. Computational research on intrinsic motivation is being complemented by, and closely interacting with, research that aims to build hierarchical architectures capable of acquiring, storing, and exploiting the knowledge and skills acquired through intrinsically motivated learning. Now is an important moment in the study of intrinsically motivated open-ended development, requiring contributions and integration across a large number of fields within the cognitive sciences. This Research Topic aims to contribute to this effort by welcoming papers carried out with ethological, psychological, neuroscientific and computational approaches, as well as research that cuts across disciplines and approaches.

Machine Learning Proceedings 1992

Author : Peter Edwards
Publisher : Morgan Kaufmann
ISBN 13 : 1483298531
Total Pages : 497 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Machine Learning Proceedings 1992 by : Peter Edwards

Download or read book Machine Learning Proceedings 1992 written by Peter Edwards and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1992

Cellular Automata

Author : Bastien Chopard
Publisher : Springer Nature
ISBN 13 : 3031149262
Total Pages : 373 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Cellular Automata by : Bastien Chopard

Download or read book Cellular Automata written by Bastien Chopard and published by Springer Nature. This book was released on 2022-08-12 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 15th International Conference on Cellular Automata for Research and Industry, ACRI 2022, which took place in Geneva, Switzerland, in September 2022. The 31 full papers presented in this volume were carefully reviewed and selected from 36 submissions. They were organized in topical sections named: Theory; Modelling and simulation physical systems and phenomena; Cellular automata and spreading dynamics; Crowds, pedestrian and traffic dynamics; Other studies on cellular automata.

Efficient Reinforcement Learning Using Gaussian Processes

Author : Marc Peter Deisenroth
Publisher : KIT Scientific Publishing
ISBN 13 : 3866445695
Total Pages : 226 pages
Book Rating : 4.8/5 (664 download)

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Book Synopsis Efficient Reinforcement Learning Using Gaussian Processes by : Marc Peter Deisenroth

Download or read book Efficient Reinforcement Learning Using Gaussian Processes written by Marc Peter Deisenroth and published by KIT Scientific Publishing. This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.

An Introduction to Deep Reinforcement Learning

Author : Vincent Francois-Lavet
Publisher : Foundations and Trends (R) in Machine Learning
ISBN 13 : 9781680835380
Total Pages : 156 pages
Book Rating : 4.8/5 (353 download)

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Book Synopsis An Introduction to Deep Reinforcement Learning by : Vincent Francois-Lavet

Download or read book An Introduction to Deep Reinforcement Learning written by Vincent Francois-Lavet and published by Foundations and Trends (R) in Machine Learning. This book was released on 2018-12-20 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.

Similarity-Based Clustering

Author : Thomas Villmann
Publisher : Springer Science & Business Media
ISBN 13 : 3642018041
Total Pages : 211 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Similarity-Based Clustering by : Thomas Villmann

Download or read book Similarity-Based Clustering written by Thomas Villmann and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the outcome of the Dagstuhl Seminar on "Similarity-Based Clustering" held at Dagstuhl Castle, Germany, in Spring 2007. In three chapters, the three fundamental aspects of a theoretical background, the representation of data and their connection to algorithms, and particular challenging applications are considered. Topics discussed concern a theoretical investigation and foundation of prototype based learning algorithms, the development and extension of models to directions such as general data structures and the application for the domain of medicine and biology. Similarity based methods find widespread applications in diverse application domains, including biomedical problems, but also in remote sensing, geoscience or other technical domains. The presentations give a good overview about important research results in similarity-based learning, whereby the character of overview articles with references to correlated research articles makes the contributions particularly suited for a first reading concerning these topics.

Advanced Information Systems Engineering

Author : Schahram Dustdar
Publisher : Springer Nature
ISBN 13 : 3030494357
Total Pages : 587 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Advanced Information Systems Engineering by : Schahram Dustdar

Download or read book Advanced Information Systems Engineering written by Schahram Dustdar and published by Springer Nature. This book was released on 2020-06-02 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020, held in Grenoble, France, in June 2020.* The 33 full papers presented in this volume were carefully reviewed and selected from 185 submissions. The book also contains one invited talk in full paper length. The papers were organized in topical sections named: distributed applications; AI and big data in IS; process mining and analysis; requirements and modeling; and information systems engineering. Abstracts on the CAiSE 2020 tutorials can be found in the back matter of the volume. *The conference was held virtually due to the COVID-19 pandemic.

Hands-On Reinforcement Learning for Games

Author : Micheal Lanham
Publisher : Packt Publishing Ltd
ISBN 13 : 1839216778
Total Pages : 420 pages
Book Rating : 4.8/5 (392 download)

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Book Synopsis Hands-On Reinforcement Learning for Games by : Micheal Lanham

Download or read book Hands-On Reinforcement Learning for Games written by Micheal Lanham and published by Packt Publishing Ltd. This book was released on 2020-01-03 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook Description With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications. What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is for If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.

Handbook of Reinforcement Learning and Control

Author : Kyriakos G. Vamvoudakis
Publisher : Springer Nature
ISBN 13 : 3030609901
Total Pages : 833 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Handbook of Reinforcement Learning and Control by : Kyriakos G. Vamvoudakis

Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by Springer Nature. This book was released on 2021-06-23 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

Decentralised Reinforcement Learning in Markov Games

Author : Peter Vrancx
Publisher : ASP / VUBPRESS / UPA
ISBN 13 : 9054877154
Total Pages : 218 pages
Book Rating : 4.0/5 (548 download)

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Book Synopsis Decentralised Reinforcement Learning in Markov Games by : Peter Vrancx

Download or read book Decentralised Reinforcement Learning in Markov Games written by Peter Vrancx and published by ASP / VUBPRESS / UPA. This book was released on 2011 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing a new approach to multiagent reinforcement learning and distributed artificial intelligence, this guide shows how classical game theory can be used to compose basic learning units. This approach to creating agents has the advantage of leading to powerful, yet intuitively simple, algorithms that can be analyzed. The setup is demonstrated here in a number of different settings, with a detailed analysis of agent learning behaviors provided for each. A review of required background materials from game theory and reinforcement learning is also provided, along with an overview of related multiagent learning methods.

Machine Learning for Decision Sciences with Case Studies in Python

Author : S. Sumathi
Publisher : CRC Press
ISBN 13 : 1000590933
Total Pages : 477 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Machine Learning for Decision Sciences with Case Studies in Python by : S. Sumathi

Download or read book Machine Learning for Decision Sciences with Case Studies in Python written by S. Sumathi and published by CRC Press. This book was released on 2022-07-08 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.

Democracy and Education

Author : John Dewey
Publisher : Createspace Independent Publishing Platform
ISBN 13 :
Total Pages : 456 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Democracy and Education by : John Dewey

Download or read book Democracy and Education written by John Dewey and published by Createspace Independent Publishing Platform. This book was released on 1916 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: . Renewal of Life by Transmission. The most notable distinction between living and inanimate things is that the former maintain themselves by renewal. A stone when struck resists. If its resistance is greater than the force of the blow struck, it remains outwardly unchanged. Otherwise, it is shattered into smaller bits. Never does the stone attempt to react in such a way that it may maintain itself against the blow, much less so as to render the blow a contributing factor to its own continued action. While the living thing may easily be crushed by superior force, it none the less tries to turn the energies which act upon it into means of its own further existence. If it cannot do so, it does not just split into smaller pieces (at least in the higher forms of life), but loses its identity as a living thing. As long as it endures, it struggles to use surrounding energies in its own behalf. It uses light, air, moisture, and the material of soil. To say that it uses them is to say that it turns them into means of its own conservation. As long as it is growing, the energy it expends in thus turning the environment to account is more than compensated for by the return it gets: it grows. Understanding the word "control" in this sense, it may be said that a living being is one that subjugates and controls for its own continued activity the energies that would otherwise use it up. Life is a self-renewing process through action upon the environment.

Intelligent Data Engineering and Automated Learning

Author : Jiming Liu
Publisher : Springer
ISBN 13 : 3540450807
Total Pages : 1161 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Intelligent Data Engineering and Automated Learning by : Jiming Liu

Download or read book Intelligent Data Engineering and Automated Learning written by Jiming Liu and published by Springer. This book was released on 2003-09-09 with total page 1161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the throughly refereed post-proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003, held in Hong Kong, China in March 2003. The 164 revised papers presented were carefully reviewed and selected from 321 submissions; for inclusion in this post-proceedings another round of revision was imposed. The papers are organized in topical sections an agents, automated learning, bioinformatics, data mining, multimedia information, and financial engineering.

Decision Making Under Uncertainty

Author : Mykel J. Kochenderfer
Publisher : MIT Press
ISBN 13 : 0262331713
Total Pages : 350 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer

Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.