Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym / Nimish Sanghi.
2021
Q325.6
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Details
Title
Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym / Nimish Sanghi.
Author
Sanghi, Nimish.
ISBN
9781484268094 (electronic bk.)
1484268091 (electronic bk.)
1484268083
9781484268087
1484268091 (electronic bk.)
1484268083
9781484268087
Publication Details
[Place of publication not identified] : Apress, 2021.
Language
English
Description
1 online resource
Item Number
10.1007/978-1-4842-6809-4 doi
Call Number
Q325.6
Dewey Decimal Classification
006.3/1
Summary
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 OpenAIs 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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Includes index.
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Deep reinforcement learning with Python.
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Table of Contents
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.
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.