Deep Reinforcement Learning Hands-On
暫譯: 深度強化學習實戰
Maxim Lapan
- 出版商: Packt Publishing
- 出版日期: 2018-06-20
- 定價: $1,980
- 售價: 6.0 折 $1,188
- 語言: 英文
- 頁數: 546
- 裝訂: Paperback
- ISBN: 1788834240
- ISBN-13: 9781788834247
-
相關分類:
Reinforcement、DeepLearning
-
相關翻譯:
動手做深度強化學習 (Deep Reinforcement Learning Hands-On) (繁中版)
-
其他版本:
Deep Reinforcement Learning Hands-On, 2/e (Paperback)
買這商品的人也買了...
-
$3,500$3,325 -
$1,980$1,881 -
$3,420Multi-Agent Machine Learning: A Reinforcement Approach
-
$1,098Introduction to Computation and Programming Using Python: With Application to Understanding Data, 2/e (Paperback)
-
$1,617Deep Learning (Hardcover)
-
$1,890$1,796 -
$990Hands-On Machine Learning with Scikit-Learn and TensorFlow (Paperback)
-
$580$458 -
$1,088Python Machine Learning, 2/e (Paperback)
-
$1,870$1,777 -
$500$390 -
$450$356 -
$1,960Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python
-
$474$450 -
$580$493 -
$1,660$1,577 -
$1,750$1,715 -
$352Python 強化學習實戰 : 應用 OpenAI Gym 和 TensorFlow 精通強化學習和深度強化學習
-
$500$390 -
$650$514 -
$380$342 -
$650$553 -
$1,235Introduction to Natural Language Processing (Hardcover)
-
$690$345 -
$650$514
相關主題
商品描述
Key Features
- A no-holds-barred introduction to reinforcement learning from the first principles to the latest and greatest algorithms
- Discover how to implement fresh RL algorithms and make them part of your project
- Learn the boundaries and applications of an area so new that algorithms and approaches are invented every month
Book Description
Reinforcement Learning (RL) is much more than the newest buzzword in deep learning. Like most areas in machine learning, the first popular texts have been around since the late 90s, but it is only since Google started to use RL algorithms to play and defeat well-known computer games, that the field shot to prominence.
This is the first book to present RL from the first principles. It presents RL algorithms and methods developed since the late 90s, in an accessible and practical fashion. RL stands for the art of coding intelligent learning agents able to adapt to a formidable array of tasks.
Max Lapan leads you through some well-known areas such as the Bellman equation and dynamic programming, and also introduces Deep-Q Network problems and Policy Gradient approaches in some depth. Max ends with a ride through some of the recent developments in RL, suggesting applications and new departures.
What you will learn
- Understand the deep learning context of RL
- See how to implement simple RL techniques such as the Bellman equation
- Apply Policy Gradient approaches to the real world
- Defeat computer games without ever touching a keyboard
- Learn the required deep learning and machine learning methods to understand RL
商品描述(中文翻譯)
**主要特點**
- 從基本原理到最新最優秀的演算法,無所不包地介紹強化學習
- 探索如何實現新穎的強化學習演算法並將其納入您的專案
- 了解這個領域的界限和應用,因為每個月都有新的演算法和方法被發明出來
**書籍描述**
強化學習(Reinforcement Learning, RL)不僅僅是深度學習中的最新流行詞彙。與機器學習中的大多數領域一樣,最早的熱門文本自90年代末就已經出現,但自從Google開始使用RL演算法來玩並擊敗知名電腦遊戲後,這個領域才迅速崛起。
這是第一本從基本原理介紹強化學習的書籍。它以易於理解和實用的方式介紹了自90年代末以來發展的RL演算法和方法。RL代表編寫能夠適應各種艱鉅任務的智能學習代理的藝術。
Max Lapan將帶您深入一些知名領域,如貝爾曼方程(Bellman equation)和動態規劃(dynamic programming),並深入介紹深度Q網絡(Deep-Q Network)問題和策略梯度(Policy Gradient)方法。Max最後將帶您了解一些強化學習的最新發展,並提出應用和新的方向。
**您將學到的內容**
- 理解強化學習的深度學習背景
- 了解如何實現簡單的強化學習技術,如貝爾曼方程
- 將策略梯度方法應用於現實世界
- 在不接觸鍵盤的情況下擊敗電腦遊戲
- 學習理解強化學習所需的深度學習和機器學習方法