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Deep reinforcement learning with Pyt...

Sanghi, Nimish.

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Deep reinforcement learning with Python = with PyTorch, TensorFlow and OpenAI Gym /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep reinforcement learning with Python/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Reinforcement learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-6809-4
ISBN:	9781484268094

Deep reinforcement learning with Python = with 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

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

1006373
Reinforcement learning.

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

Dewey Class. No.: 006.31

Deep reinforcement learning with Python = with PyTorch, TensorFlow and OpenAI Gym /
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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.
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