Reinforcement Learning: Industrial Applications of Intelligent Agents
暫譯: 強化學習：智能代理的工業應用

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
  • 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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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（Reinforcement Learning, RL）將在未來十年內帶來人工智慧（AI）最大的突破之一，使演算法能夠從環境中學習以達成任意目標。這一令人振奮的發展避免了傳統機器學習（Machine Learning, ML）演算法中的限制。本書實用地向數據科學和AI專業人士展示如何執行強化過程，使機器能夠自我學習。

作者菲爾·溫德博士（Dr. Phil Winder）來自Winder Research，涵蓋了從基本構建塊到最先進實踐的所有內容。您將探索RL的當前狀態，重點關注工業應用，並學習眾多演算法、框架和環境。本書不是一本食譜書——它不會迴避數學，並且期望讀者對ML有一定的熟悉度。

- 了解什麼是RL以及演算法如何幫助解決問題
- 熟悉RL的基本概念，包括馬可夫決策過程（Markov Decision Processes）、動態規劃（Dynamic Programming）和時間差學習（Temporal Difference Learning）
- 深入探討價值方法（Value Methods）和策略梯度方法（Policy Gradient Methods）
- 應用先進的RL實現，如元學習（Meta Learning）、層次學習（Hierarchical Learning）、進化演算法（Evolutionary Algorithms）和模仿學習（Imitation Learning）
- 理解尖端的深度RL演算法，包括Rainbow、PPO、TD3、SAC等
- 通過附帶的Git代碼庫獲得實用範例

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。作為位於英國的雲端原生資料科學顧問公司 Winder Research 的執行長，他幫助初創公司和企業利用資料科學。透過顧問服務和開發的結合，他們能夠透過改善產品和平台來成長和擴展業務。

在過去的五年中，Phil 在各種資料科學訓練課程中教導了數千名工程師，這些課程在會議、公共場合、私人場合以及在線的 Safari 學習平台上進行。在這些課程中，Phil 專注於在行業中使用資料科學的實務，涵蓋從數據清理到深度強化學習等廣泛主題。

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