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Deep Reinforcement Learning: Fundamentals, Research and Applications

Original price was: $139.00.Current price is: $48.65.

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.Product details Publisher: Springer; 1st ed. 2020 edition (June 29, 2020) Publication Date: June 29, 2020

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Deep Reinforcement Learning: Fundamentals Research and Applications

$25.00

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of Alpha Go. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.Deep Reinforcement Learning: Fundamentals, Research and Applications 1st Edition is written by Hao Dong; Zihan Ding; Shanghang Zhang and published by Springer. ISBNs for Deep Reinforcement Learning are 9789811540950, 9811540950 and the print ISBNs are 9789811540943, 9811540942.

Order Processing Time

Shipping Methods and Carriers

Zetlly partners exclusively with reputable shipping carriers to ensure timely delivery of your orders. We utilize:

FedEx
UPS
USPS

The choice of carrier is determined by factors such as destination, weight, and delivery timeframe to provide optimal service.

Shipping Rates and Fees

Free shipping is provided for all orders over $199.
Orders under $199 will incur a flat-rate shipping fee of $7.99.
All orders shipped within the United States will be subject to a sales tax charge of 5%.

Estimated Delivery Time

Shipping Restrictions

Tracking Your Order

Please allow up to 48 hours for tracking information to update in the carrier’s system.

Eligibility for Returns and Exchanges

How to Return or Exchange an Item

To initiate a return or exchange, please follow these steps:

Contact our customer support at contact@zetlly.com with your order number and reason for return or exchange.
Our team will respond within 24 hours to provide detailed instructions, including the specific Return Address for your shipment.
Package your item securely and include all original packaging and proof of purchase.

Return shipments should be sent to: Blanq LLC 1201 South Hope Street Apt 2413, Los Angeles, CA 90015, USA

Return Conditions

Items must be returned in their original condition, unworn, undamaged, and complete with all original packaging and documentation.
Items returned without prior authorization or not meeting the above conditions may not qualify for a refund or exchange.

Return Shipping Costs

Non-Returnable Items

The following items cannot be returned:

Digital products (e-books or downloadable content)
Personalized or customized items
Gift cards

Accepted Payment Methods

Zetlly accepts the following secure and widely trusted payment options:

PayPal: Easily pay through your PayPal account, benefiting from secure transactions and buyer protection.
Stripe: Pay securely using major credit and debit cards including Visa, MasterCard, American Express, and Discover via Stripe’s encrypted payment gateway.

Payment Security

Zetlly does not store any credit card or sensitive financial information directly on our servers, further enhancing the security and protection of your personal data.

Payment Process and Confirmation

Please retain this confirmation email for your records and reference in case of any inquiries or disputes.

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of Alpha Go. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.Deep Reinforcement Learning: Fundamentals, Research and Applications 1st Edition is written by Hao Dong; Zihan Ding; Shanghang Zhang and published by Springer. ISBNs for Deep Reinforcement Learning are 9789811540950, 9811540950 and the print ISBNs are 9789811540943, 9811540942.