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Deep reinforcement learning with Pyt...

Sanghi, Nimish.

Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym

紀錄類型:	書目-語言資料,印刷品 : 單行本
副題名:	with Pytorch, Tensorflow and OpenAI Gym
作者:	SanghiNimish.,
出版地:	[New York]
出版者:	Apress;
出版年:	c2021.
面頁冊數:	xix, 382 p.ill. : 26 cm.;
標題:	Reinforcement learning. -
標題:	Python (Computer program language) -
附註:	Includes index.
摘要註:	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.
ISBN:	9781484268087
內容註:	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 with Python : with Pytorch, Tensorflow and OpenAI Gym
Sanghi, Nimish.

Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym / Nimish Sanghi. - [New York] : Apress, c2021.. - xix, 382 p. ; ill. ; 26 cm..
Chapter 1: Introduction to Deep Reinforcement Learning.
Includes index..
ISBN 9781484268087ISBN 1484268083
Reinforcement learning.Python (Computer program language)

Deep reinforcement learning with Python : with Pytorch, Tensorflow and OpenAI Gym
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