Deep Reinforcement Learning Hands-On

Maxim Lapan

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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偉大的算法,全面介紹強化學習

  • 探索如何實現新的強化學習算法並將其應用於項目中

  • 了解一個如此新穎的領域的範圍和應用,以至於每個月都會有新的算法和方法被發明

書籍描述

強化學習(RL)不僅僅是深度學習中最新的流行詞。像機器學習中的大多數領域一樣,第一批流行的文本從90年代末就已經存在,但直到Google開始使用RL算法來玩並擊敗著名的電腦遊戲,這個領域才受到關注。

這是第一本從基本原理介紹RL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算法和方法。RL代表編寫能夠適應各種任務的智能學習代理的藝術。

Max Lapan引領您深入了解一些著名的領域,如Bellman方程和動態規劃,並在一些深度上介紹了Deep-Q網絡問題和策略梯度方法。Max最後介紹了RL的一些最新發展,並提出應用和新的方向。

你將學到什麼


  • 了解RL的深度學習背景

  • 了解如何實現簡單的RL技術,如Bellman方程

  • 應用策略梯度方法於現實世界

  • 在不觸摸鍵盤的情況下擊敗電腦遊戲

  • 學習所需的深度學習和機器學習方法以理解RL