A Markovian Decision Model for Evaluating Training Programs for the Mentally Retarded PDF Download
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Author: Jack Ray Meredith
Publisher:
ISBN:
Category :
Languages : en
Pages : 210
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Book Description
Author: Jack Ray Meredith
Publisher:
ISBN:
Category :
Languages : en
Pages : 210
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Book Description
Author: American Assembly of Collegiate Schools of Business
Publisher:
ISBN:
Category : Business teachers
Languages : en
Pages : 646
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 492
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Author:
Publisher:
ISBN:
Category : Mental health
Languages : en
Pages : 488
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Book Description
Author: Xerox University Microfilms
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 812
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Author:
Publisher:
ISBN:
Category : Education
Languages : en
Pages : 796
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Book Description
Author: N. K. Kwak
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 368
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Book Description
This collection of articles illustrates the application of operations research to aid in health care decision making and planning. The articles provide an insight into not only the application of the techniques themselves but also to the process of actually developing and implementing the models for effective decision making. This helpful text will be of interest to hospital administrators, health care planners, consultants, and students.
Author: Xerox University Microfilms
Publisher:
ISBN:
Category : Business
Languages : en
Pages : 814
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Book Description
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:
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Category : Union catalogs
Languages : en
Pages : 616
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Book Description
Includes entries for maps and atlases.
