Review on use of Reinforcement Learning in Artificial Intelligence Author
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Review on use of Reinforcement Learning in Artificial Intelligence
DE NW EB DL
ISBN: 9783656219095 bzw. 3656219095, in Deutsch, GRIN Verlag, neu, E-Book, elektronischer Download.
Lieferung aus: Deutschland, E-Book zum Download.
Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery involving Artificial Intelligence. Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Reinforcement Learning (RL) comes from the animal learning theory. RL does not need prior knowledge, it can autonomously get optional policy with the knowledge obtained by trial- and-error and continuously interact with dynamic environment. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the problem, an agent is supposed decide the best action to select based on its current state. When this step is repeated, the problem is known as a Markov Decision Process. A Markov Decision Process is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action that is available in states. Markov Decision Process provides a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker.
Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery involving Artificial Intelligence. Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Reinforcement Learning (RL) comes from the animal learning theory. RL does not need prior knowledge, it can autonomously get optional policy with the knowledge obtained by trial- and-error and continuously interact with dynamic environment. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the problem, an agent is supposed decide the best action to select based on its current state. When this step is repeated, the problem is known as a Markov Decision Process. A Markov Decision Process is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action that is available in states. Markov Decision Process provides a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker.
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Review on use of Reinforcement Learning in Artificial Intelligence Mehdi Samieiyeganeh Author (2012)
~EN NW EB DL
ISBN: 9783656219095 bzw. 3656219095, vermutlich in Englisch, GRIN Publishing, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd.
Research Paper (postgraduate) from the year 2012 in the subject Engineering - Artificial Intelligence, grade: none, Jawaharlal Nehru University , language: English, abstract: Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery involving Artificial Intelligence. Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Reinforcement Learning (RL) comes from the animal learning theory. RL does not need prior knowledge, it can autonomously get optional policy with the knowledge obtained by trial- and-error and continuously interact with dynamic environment. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the problem, an agent is supposed decide the best action to select based on its current state. When this step is repeated, the problem is known as a Markov Decision Process. A Markov Decision Process is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action that is available in state's'. Markov Decision Process provides a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker.
Research Paper (postgraduate) from the year 2012 in the subject Engineering - Artificial Intelligence, grade: none, Jawaharlal Nehru University , language: English, abstract: Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery involving Artificial Intelligence. Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Reinforcement Learning (RL) comes from the animal learning theory. RL does not need prior knowledge, it can autonomously get optional policy with the knowledge obtained by trial- and-error and continuously interact with dynamic environment. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the problem, an agent is supposed decide the best action to select based on its current state. When this step is repeated, the problem is known as a Markov Decision Process. A Markov Decision Process is a discrete time stochastic control process. At each time step, the process is in some state s, and the decision maker may choose any action that is available in state's'. Markov Decision Process provides a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker.
3
Review on use of Reinforcement Learning in Artificial Intelligence
DE NW
ISBN: 9783656219095 bzw. 3656219095, in Deutsch, GRIN Verlag GmbH, neu.
Lieferung aus: Deutschland, sofort lieferbar.
2012, 6 Seiten, Englisch, Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery involving Artificial Intelligence. Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Reinforcement Learning (RL) comes from the animal learning theory. RL does not need prior knowledge, it can autonomously get optional policy with the knowledge obtained by trial-and-error and continuously interact with dynamic environment. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the pr.
2012, 6 Seiten, Englisch, Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery involving Artificial Intelligence. Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Reinforcement Learning (RL) comes from the animal learning theory. RL does not need prior knowledge, it can autonomously get optional policy with the knowledge obtained by trial-and-error and continuously interact with dynamic environment. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the pr.
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Review on use of Reinforcement Learning in Artificial Intelligence (2012)
DE NW EB DL
ISBN: 9783656219095 bzw. 3656219095, in Deutsch, GRIN Verlag, neu, E-Book, elektronischer Download.
Lieferung aus: Deutschland, Versandkostenfrei.
Review on use of Reinforcement Learning in Artificial Intelligence: Research Paper (postgraduate) from the year 2012 in the subject Engineering - Artificial Intelligence, grade: none, Jawaharlal Nehru University , language: English, abstract: Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as ... Englisch, Ebook.
Review on use of Reinforcement Learning in Artificial Intelligence: Research Paper (postgraduate) from the year 2012 in the subject Engineering - Artificial Intelligence, grade: none, Jawaharlal Nehru University , language: English, abstract: Human started making machinery that can do the job for them. The technology developed so much that it started involving many other branches of engineering such as ... Englisch, Ebook.
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Review on use of Reinforcement Learning in Artificial Intelligence (2012)
EN NW EB DL
ISBN: 9783656219095 bzw. 3656219095, in Englisch, GRIN Publishing, GRIN Publishing, GRIN Publishing, neu, E-Book, elektronischer Download.
Lieferung aus: Kanada, in-stock.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
6
Review on use of Reinforcement Learning in Artificial Intelligence
EN NW EB DL
ISBN: 9783656219095 bzw. 3656219095, in Englisch, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigte Staaten von Amerika, zzgl. Versandkosten, Free Shipping on eligible orders over $25.
Mehdi Samieiyeganeh, Parisa Bahraminikoo, G. Praveen Babu, NOOK Book (eBook), Edition: 1, English-language edition,.
Mehdi Samieiyeganeh, Parisa Bahraminikoo, G. Praveen Babu, NOOK Book (eBook), Edition: 1, English-language edition,.
7
Review on use of Reinforcement Learning in Artificial Intelligence
DE NW EB
ISBN: 9783656219095 bzw. 3656219095, in Deutsch, GRIN Publishing, neu, E-Book.
Lieferung aus: Vereinigte Staaten von Amerika, Lagernd.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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