From Shortest Paths to Reinforcement Learning PDF Download
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Author: Paolo Brandimarte
Publisher: Springer Nature
ISBN: 3030618676
Category : Business & Economics
Languages : en
Pages : 216
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Book Description
Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.
Author: Paolo Brandimarte
Publisher: Springer Nature
ISBN: 3030618676
Category : Business & Economics
Languages : en
Pages : 216
Get Book Here
Book Description
Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.
Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
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.
Author: Jian Dong
Publisher: Springer Nature
ISBN: 981972757X
Category :
Languages : en
Pages : 655
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Book Description
Author: Paolo Brandimarte
Publisher:
ISBN: 9783030618681
Category :
Languages : en
Pages : 0
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Book Description
Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.
Author: Bhavatarini N
Publisher: MileStone Research Publications
ISBN: 9360130087
Category : Computers
Languages : en
Pages : 144
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Book Description
Reinforcement learning (RL) is a subfield of machine learning that deals with how an agent should learn to take actions in an environment to maximize some notion of cumulative reward. In other words, reinforcement learning is a learning paradigm where an agent learns to interact with an environment by taking actions and observing the feedback it receives in the form of rewards or penalties. It is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty.
Author: Philip Agre
Publisher: MIT Press
ISBN: 9780262510905
Category : Computers
Languages : en
Pages : 794
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Book Description
Over time the field of artificial intelligence has developed an "agent perspective" expanding its focus from thought to action, from search spaces to physical environments, and from problem-solving to long-term activity. Originally published as a special double volume of the journal Artificial Intelligence, this book brings together fundamental work by the top researchers in artificial intelligence, neural networks, computer science, robotics, and cognitive science on the themes of interaction and agency. It identifies recurring themes and outlines a methodology of the concept of "agency." The seventeen contributions cover the construction of principled characterizations of interactions between agents and their environments, as well as the use of these characterizations to guide analysis of existing agents and the synthesis of artificial agents.Artificial Intelligence series.Special Issues of Artificial Intelligence
Author: Yousef Farhaoui
Publisher: Springer Nature
ISBN: 3031484657
Category :
Languages : en
Pages : 590
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Book Description
Author: Yi Qu
Publisher: Springer Nature
ISBN: 981971091X
Category :
Languages : en
Pages : 526
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Book Description
Author: David Hausheer
Publisher: Springer Science & Business Media
ISBN: 3540705864
Category : Business & Economics
Languages : en
Pages : 227
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Book Description
This book constitutes the refereed proceedings of the Second International Conference on Autonomous Infrastructure, Management and Security, AIMS 2008, held in Bremen, Germany, in June 2008, under the auspices of IFIP. The 13 revised full papers presented together with 8 papers of the AIMS PhD workshop were carefully reviewed and selected from 33 submissions to the main conference and 12 papers for the PhD workshop respectively. The papers are discussing topics such as autonomy, incentives and trust, overlays and virtualization, load balancing and fault recovery, network traffic engineering and analysis, and convergent behavior of distributed systems.
Author: Sean Meyn
Publisher: Cambridge University Press
ISBN: 1316511960
Category : Business & Economics
Languages : en
Pages : 453
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Book Description
A how-to guide and scientific tutorial covering the universe of reinforcement learning and control theory for online decision making.
