Deep Reinforcement Learning with Python: With Pytorch, Tensorflow and 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發展的領域，在自動駕駛車輛、機器人、醫療保健、金融等領域都有顯著影響。本書介紹了使用深度 Q 學習和策略梯度模型的深度強化學習，並提供編程練習。

首先，您將回顧馬爾可夫決策過程、貝爾曼方程和動態規劃等構成深度強化學習核心概念和基礎的內容。接下來，您將學習無模型學習，並使用神經網絡和深度學習進行函數逼近。然後，介紹各種深度強化學習算法，如深度 Q 網絡、各種演員-評論家方法和其他基於策略的方法。

您還將研究在強化學習算法中的探索與利用的困境，以及在 AlphaGo 成功中起關鍵作用的蒙特卡羅樹搜索（MCTS）。最後，本書使用流行的深度學習框架 TensorFlow 和 PyTorch 進行深度強化學習實現。最終，您將了解深度強化學習以及使用 TensorFlow、PyTorch 和 Open AI Gym 實現深度 Q 網絡和策略梯度模型。

本書的學習目標：
- 研究深度強化學習
- 使用 OpenAI 的 Gym 環境實現深度學習算法
- 使用演員-評論家算法編寫自己的 Atari 遊戲代理
- 應用最佳實踐進行模型構建和算法訓練

本書適合對象：
- 機器學習開發人員和架構師，希望在人工智能和深度學習領域保持領先。

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，致力於運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曾在普華永道、IBM和Oracle擔任負責利潤和損益的領導職位。2006年，他在波士頓、芝加哥和班加羅爾創立了SOAIS軟體諮詢公司，開啟了他的創業之旅。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遷移到雲端。

他還是一位在人工智慧和自動化驅動的初創企業領域的天使投資者。他共同創辦了Paybooks，一個針對印度市場的SaaS人力資源和薪資平台。他還共同創辦了一家位於波士頓的初創企業，提供AI驅動的工作流程和視頻行銷自動化平台ZipperAgent和ZipperHQ。他目前擔任這兩個平台的首席技術官和首席數據科學家。

Nimish擁有印度艾哈邁達巴德管理學院的工商管理碩士學位，以及印度坎普爾理工學院的電機工程學士學位。他還擁有多個人工智慧和深度學習的認證。

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