國立高雄大學圖資館 |

Language: English

Back

Deep reinforcement learning with Pyt...

Sanghi, Nimish.

Deep reinforcement learning with Pythonwith PyTorch, TensorFlow and OpenAI Gym /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep reinforcement learning with Pythonby Nimish Sanghi.
Reminder of title:	with PyTorch, TensorFlow and OpenAI Gym /
Author:	Sanghi, Nimish.
Published:	Berkeley, CA :Apress :2021.
Description:	xix, 382 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Reinforcement learning.
Online resource:	https://doi.org/10.1007/978-1-4842-6809-4
ISBN:	9781484268094$q(electronic bk.)

Deep reinforcement learning with Pythonwith PyTorch, TensorFlow and OpenAI Gym /
Sanghi, Nimish.

Deep reinforcement learning with Pythonwith PyTorch, TensorFlow and OpenAI Gym /[electronic resource] :by Nimish Sanghi. - Berkeley, CA :Apress :2021. - xix, 382 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Deep Reinforcement Learning -- Chapter 2: Markov Decision Processes -- Chapter 3: Model Based Algorithms -- Chapter 4: Model Free Approaches -- Chapter 5: Function Approximation -- Chapter 6:Deep Q-Learning -- Chapter 7: Policy Gradient Algorithms -- Chapter 8: Combining Policy Gradients and Q-Learning -- Chapter 9: Integrated Learning and Planning -- Chapter 10: Further Exploration and Next Steps.

Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods. You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym. You will: Examine deep reinforcement learning Implement deep learning algorithms using OpenAI's Gym environment Code your own game playing agents for Atari using actor-critic algorithms Apply best practices for model building and algorithm training.

ISBN: 9781484268094$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6809-4doiSubjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6 / .S264 2021

Dewey Class. No.: 006.31

Deep reinforcement learning with Pythonwith PyTorch, TensorFlow and OpenAI Gym /
LDR:02972nmm a2200325 a 4500 001 599889
003 DE-He213
005 20210719142419.0
006 m d
007 cr nn 008maaau
008 211027s2021 cau s 0 eng d
020 $a 9781484268094$q(electronic bk.)
020 $a 9781484268087$q(paper)
024 7 $a 10.1007/978-1-4842-6809-4 $2 doi
035 $a 978-1-4842-6809-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.6 $b .S264 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.6 $b .S225 2021
100 1 $a Sanghi, Nimish. $3 894230
245 1 0 $a Deep reinforcement learning with Python $h [electronic resource] : $b with PyTorch, TensorFlow and OpenAI Gym / $c by Nimish Sanghi.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xix, 382 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction to Deep Reinforcement Learning -- Chapter 2: Markov Decision Processes -- Chapter 3: Model Based Algorithms -- Chapter 4: Model Free Approaches -- Chapter 5: Function Approximation -- Chapter 6:Deep Q-Learning -- Chapter 7: Policy Gradient Algorithms -- Chapter 8: Combining Policy Gradients and Q-Learning -- Chapter 9: Integrated Learning and Planning -- Chapter 10: Further Exploration and Next Steps.
520 $a Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods. You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym. You will: Examine deep reinforcement learning Implement deep learning algorithms using OpenAI's Gym environment Code your own game playing agents for Atari using actor-critic algorithms Apply best practices for model building and algorithm training.
650 0 $a Reinforcement learning. $3 349131
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Python. $3 763308
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6809-4
950 $a Professional and Applied Computing (SpringerNature-12059)