Deep Reinforcement Learning with Python, 2/e (Paperback)

Ravichandiran, Sudharsan

  • 出版商: Packt Publishing
  • 出版日期: 2020-09-30
  • 售價: $1,870
  • 貴賓價: 9.5$1,777
  • 語言: 英文
  • 頁數: 760
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1839210680
  • ISBN-13: 9781839210686
  • 相關分類: Python程式語言ReinforcementDeepLearning
  • 立即出貨 (庫存=1)

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商品描述

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms

 

Key Features

  • Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm
  • Learn how to implement algorithms with code by following examples with line-by-line explanations
  • Explore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrations

 

Book Description

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.

In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples.

The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI's baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.

By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.

 

What you will learn

  • Understand core RL concepts including the methodologies, math, and code
  • Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym
  • Train an agent to play Ms Pac-Man using a Deep Q Network
  • Learn policy-based, value-based, and actor-critic methods
  • Master the math behind DDPG, TD3, TRPO, PPO, and many others
  • Explore new avenues such as the distributional RL, meta RL, and inverse RL
  • Use Stable Baselines to train an agent to walk and play Atari games

 

Who this book is for

If you're a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you.

Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.

商品描述(中文翻譯)

一本以豐富範例為特色的指南,適合初學者開始他們的強化學習和深度強化學習之旅,並使用最先進的不同算法。

主要特點:
- 涵蓋廣泛的基礎到高級強化學習算法,並對每個算法進行數學解釋
- 通過遵循具有逐行解釋的示例代碼來學習如何實現算法
- 探索最新的強化學習方法,如DDPG、PPO和專家示範的使用

書籍描述:
近年來,強化學習算法的質量和數量有了顯著提升,這本《Python實戰強化學習》第二版已經改編成一本以範例為特色的指南,用於學習最先進的強化學習(RL)和深度強化學習算法,並使用TensorFlow 2和OpenAI Gym工具包。

除了探索強化學習基礎知識和基本概念,如Bellman方程、馬爾可夫決策過程和動態規劃算法,第二版還深入探討了價值、策略和演員評論強化學習方法的全譜。它深入研究了DQN、TRPO、PPO和ACKTR、DDPG、TD3和SAC等最先進的算法,揭示了其背後的數學原理,並通過簡單的代碼示例進行實現。

本書還新增了幾個專門介紹新的強化學習技術的章節,包括分布式強化學習、模仿學習、逆向強化學習和元強化學習。您將學習如何利用Stable Baselines,這是OpenAI基線庫的改進版本,輕鬆實現流行的強化學習算法。本書最後概述了研究中有前景的方法,如元學習和想像增強代理。

通過閱讀本書,您將能夠在實際項目中有效地應用強化學習和深度強化學習。

學到的內容:
- 理解強化學習的核心概念,包括方法論、數學和代碼
- 使用OpenAI Gym訓練代理解決Blackjack、FrozenLake和其他問題
- 使用深度Q網絡訓練代理玩Ms Pac-Man
- 學習基於策略、基於價值和演員評論方法
- 掌握DDPG、TD3、TRPO、PPO等算法背後的數學原理
- 探索分布式強化學習、元強化學習和逆向強化學習等新途徑
- 使用Stable Baselines訓練代理走路和玩Atari遊戲

適合對神經網絡沒有或很少經驗的機器學習開發人員,對人工智能感興趣並想從頭學習強化學習的讀者。需要基本的線性代數、微積分和Python編程語言的基礎知識,對TensorFlow有一些經驗則更好。