Deep Reinforcement Learning with Python

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內容簡介

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 inthe 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.
What You'll LearnExamine deep reinforcement learning Implement deep learning algorithms using OpenAI's Gym environmentCode your own game playing agents for Atari using actor-critic algorithmsApply best practices for model building and algorithm training Who This Book Is For

Machine learning developers and architects who want to stay ahead of the curve in the field of AI and deep learning.

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詳細資料

詳細資料

• • 語言
  • 英文
  • 裝訂
  • 紙本平裝
• • ISBN
  • 9781484268087
  • 分級
  • 普通級
• • 頁數
  • 0
  • 商品規格
• • 出版地
  • 美國
  • 適讀年齡
  • 全齡適讀
• • 注音
  • 級別

英文書＞自然科普＞電腦資訊＞其他電腦資訊

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