Reinforcement Learning: Industrial Applications of Intelligent Agents

D, Phil Winder

Reinforcement Learning: Industrial Applications of Intelligent Agents
  • 出版商: O'Reilly
  • 出版日期: 2020-12-15
  • 定價: $2,230
  • 售價: 9.5 折 $2,119
  • 貴賓價: 9.0 折 $2,007
  • 語言: 英文
  • 頁數: 409
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1098114833
  • ISBN-13: 9781098114831
  • 相關分類: Reinforcement、DeepLearning

    • 立即出貨 (庫存 < 3)

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相關主題

商品描述

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to perform the reinforcement process that allows a machine to learn by itself.

Author Dr. Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focusing on industrial applications, and learn numerous algorithms, frameworks, and environments. This is no cookbook--it doesn't shy away from math and expects familiarity with ML.

  • Learn what RL is and how the algorithms help solve problems
  • Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning
  • Dive deep into value methods and policy gradient methods
  • Apply advanced RL implementations such as meta learning, hierarchical learning, evolutionary algorithms, and imitation learning
  • Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more
  • Get practical examples through the accompanying Git repository

商品描述(中文翻譯)

強化學習（RL）將在未來十年內成為人工智慧領域的重大突破之一，使得算法能夠從環境中學習，實現任意目標。這一令人興奮的發展避免了傳統機器學習（ML）算法中存在的限制。這本實用書向數據科學和人工智慧專業人士展示了如何進行強化學習過程，使機器能夠自主學習。

作者Phil Winder博士介紹了從基礎構建塊到最新實踐的一切。您將探索當前強化學習的狀態，重點關注工業應用，並學習多種算法、框架和環境。這不是一本食譜書-它不回避數學，並期望讀者對機器學習有一定的了解。


  • 了解強化學習是什麼，以及這些算法如何幫助解決問題

  • 深入了解強化學習的基礎知識，包括馬爾可夫決策過程、動態規劃和時間差分學習

  • 深入研究價值方法和策略梯度方法

  • 應用高級強化學習實現，如元學習、階層學習、進化算法和模仿學習

  • 了解最先進的深度強化學習算法，包括Rainbow、PPO、TD3、SAC等

  • 通過附帶的Git存儲庫獲得實用示例

作者簡介

Dr. Phil Winder is a multidisciplinary Software Engineer and Data Scientist. As the CEO of Winder Research, a Cloud-Native Data Science consultancy based in the UK, he helps startups and enterprises utilise Data Science. Through a combination of consulting and development they are able to grow and scale their business by improving their products and platforms.

For the past 5 years, Phil has taught thousands of Engineers about Data Science in his range of Data Science training courses at conferences, in public, in private and on the online Safari learning platform. In these courses Phil focuses on the practicalities of using Data Science in industry on a wide range of topics from cleaning data all the way through to deep reinforcement learning.

作者簡介(中文翻譯)

Dr. Phil Winder是一位多學科的軟體工程師和資料科學家。作為位於英國的Winder Research的首席執行官，他協助初創企業和企業利用資料科學。通過咨詢和開發的結合，他們能夠通過改進產品和平台來實現業務的增長和擴展。

在過去的5年中，Phil在各種場合，包括會議、公開場合、私人場合以及在線學習平台Safari上，教授了數千名工程師有關資料科學的培訓課程。在這些課程中，Phil專注於在行業中使用資料科學的實際操作，涵蓋了從數據清理到深度強化學習等各種主題。

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