Reinforcement Learning : Computer Science, Machine Learning, Game Theory, Bounded Rationality, Markov Decision Process, Supervised Learning, Multi-Armed Bandit, Robot Control, Monte Carlo Method

Language: English

Published by Omniscriptum, 2010

6130348177 / 9786130348175

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Inspired by related psychological theory, in computer science, reinforcement learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. In economics and game theory, reinforcement learning is considered as a boundedly rational interpretation of how equilibrium may arise. The environment is typically formulated as a finite-state Markov decision process (MDP), and reinforcement learning algorithms for this context are highly related to dynamic programming techniques. State transition probabilities and reward probabilities in the MDP are typically stochastic but stationary over the course of the problem.

Seller Inventory # 9786130348175

Title
Reinforcement Learning : Computer Science, Machine Learning, Game Theory, Bounded Rationality, Markov Decision Process, Supervised Learning, Multi-Armed Bandit, Robot Control, Monte Carlo Method
Author
Mariam T. Tennoe
Publisher
Omniscriptum
Publication year
2010
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6130348177
ISBN 13
9786130348175
Item weight
149 grams
Dimensions
220x150x6 mm

AHA-BUCH GmbH

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