Deep Reinforcement Learning with Python: With Pytorch, Tensorflow and Openai Gym
暫譯: 使用 Python 的深度強化學習：包含 Pytorch、Tensorflow 和 OpenAI Gym

Sanghi, Nimish

Deep Reinforcement Learning with Python: With Pytorch, Tensorflow and Openai Gym

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商品描述

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.
What You'll Learn

  • 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

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.

 

商品描述(中文翻譯)

深度強化學習是一個快速成長的學科，對自動駕駛車輛、機器人技術、醫療保健、金融等領域產生了重大影響。本書涵蓋了使用深度 Q 學習和策略梯度模型的深度強化學習，並包含編碼練習。

您將首先回顧馬可夫決策過程、貝爾曼方程和動態規劃，這些構成了深度強化學習的核心概念和基礎。接下來，您將學習無模型學習，然後使用神經網絡和深度學習進行函數近似。隨後將介紹各種深度強化學習算法，例如深度 Q 網絡、各種形式的演員-評論家方法以及其他基於策略的方法。

您還將探討探索與利用的困境，這是強化學習算法中的一個關鍵考量，並了解蒙特卡羅樹搜索（MCTS），它在 AlphaGo 的成功中扮演了重要角色。最後幾章將以使用流行的深度學習框架（如 TensorFlow 和 PyTorch）實現深度強化學習作結。最終，您將理解深度強化學習以及使用 TensorFlow、PyTorch 和 OpenAI Gym 實現深度 Q 網絡和策略梯度模型。

您將學到什麼


  • 檢視深度強化學習

  • 使用 OpenAI 的 Gym 環境實現深度學習算法

  • 為 Atari 編寫自己的遊戲代理，使用演員-評論家算法

  • 應用模型構建和算法訓練的最佳實踐

本書適合誰



希望在人工智慧和深度學習領域保持領先的機器學習開發者和架構師。

作者簡介

Nimish is a passionate technical leader who brings to table extreme focus on use of technology for solving customer problems. He has over 25 years of work experience in the Software and Consulting. Nimish has held leadership roles with P&L responsibilities at PwC, IBM and Oracle. In 2006 he set out on his entrepreneurial journey in Software consulting at SOAIS with offices in Boston, Chicago and Bangalore. Today the firm provides Automation and Digital Transformation services to Fortune 100 companies helping them make the transition from on-premise applications to the cloud.

 

He is also an angel investor in the space of AI and Automation driven startups. He has co-founded Paybooks, a SaaS HR and Payroll platform for Indian market. He has also cofounded a Boston based startup which offers ZipperAgent and ZipperHQ, a suite of AI driven workflow and video marketing automation platforms. He currently hold the position as CTO and Chief Data Scientist for both these platforms.

 

Nimish has an MBA from Indian Institute of Management in Ahmedabad, India and a BS in Electrical Engineering from Indian Institute of Technology in Kanpur, India. He also holds multiple certifications in AI and Deep Learning.

 

作者簡介(中文翻譯)

Nimish 是一位充滿熱情的技術領導者，他專注於利用技術解決客戶問題。他在軟體和諮詢領域擁有超過 25 年的工作經驗。Nimish 曾在 PwC、IBM 和 Oracle 擔任具有盈虧責任的領導職位。2006 年，他在 SOAIS 開始了他的創業之旅，專注於軟體諮詢，並在波士頓、芝加哥和班加羅爾設有辦公室。如今，該公司為《財富》100 強企業提供自動化和數位轉型服務，幫助他們從本地應用程式過渡到雲端。

他也是專注於人工智慧（AI）和自動化驅動的初創公司的天使投資人。他共同創立了 Paybooks，這是一個針對印度市場的 SaaS 人力資源和薪資平台。他還共同創立了一家位於波士頓的初創公司，提供 ZipperAgent 和 ZipperHQ，這是一套基於 AI 的工作流程和視頻行銷自動化平台。他目前擔任這兩個平台的首席技術官（CTO）和首席數據科學家。

Nimish 擁有印度艾哈邁達巴德印度管理學院的 MBA 學位，以及印度坎普爾印度理工學院的電機工程學士學位。他還擁有多項人工智慧和深度學習的認證。

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