ISBN: 9786131381485 - q- learning: reinforcement learning, markov decision process, probably approximately correct learning, artificial neural network (3 risultati)

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Q-learning is a reinforcement learning technique that works by learning an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter. One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment. A recent variation called delayed-Q learning has shown substantial improvements, bringing PAC bounds to Markov decision processes 116 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Q-learning is a reinforcement learning technique that works by learning an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter. One of the strengths of Q-learning is that it is able to compare the expected utility of the available actions without requiring a model of the environment. A recent variation called delayed-Q learning has shown substantial improvements, bringing PAC bounds to Markov decision processes.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. Q-learning is areinforcement learning technique that works by learning an action-valuefunction that gives the expected utility of taking a given action in agiven state and following a fixed policy thereafter. One of thestrengths of Q-learning is that it is able to compare the expectedutility of the available actions without requiring a model of theenvironment. A recent variation called delayed-Q learning has shownsubstantial improvements, bringing PAC bounds to Markov decisionprocessesVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch.…