Deep Reinforcement Learning Hands-On, 2/e (Paperback)
暫譯: 深度強化學習實戰，第2版 (平裝本)

Maxim Lapan

Deep Reinforcement Learning Hands-On, 2/e (Paperback)

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商品描述

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

 

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

 

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

 

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsoft's TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubik's Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques
  • Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
  • Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
  • Apply RL methods to cheap hardware robotics platforms

商品描述(中文翻譯)

《深度強化學習實作（第二版）》是一本更新和擴充的暢銷指南，介紹最新的強化學習（RL）工具和技術。它為您提供了強化學習的基本概念介紹，以及編寫智能學習代理以執行各種實用任務的實作能力。

本書新增六個章節，專注於強化學習中的各種最新發展，包括離散優化（解決魔術方塊）、多代理方法、微軟的 TextWorld 環境、高級探索技術等，您將從本書中深入了解這一新興領域的最新創新。

此外，您將獲得可行的見解，涵蓋深度 Q 網絡、策略梯度方法、連續控制問題以及高度可擴展的非梯度方法等主題。您還將發現如何以不到 100 美元的成本構建一個使用強化學習訓練的實際硬體機器人，並在僅需 30 分鐘的訓練中使用逐步代碼優化解決 Pong 環境。

簡而言之，《深度強化學習實作（第二版）》是您在探索強化學習的複雜性時的良伴，幫助您通過實際範例獲得經驗和知識。

- 理解強化學習的深度學習背景並實作複雜的深度學習模型
- 評估強化學習方法，包括交叉熵、DQN、演員-評論者、TRPO、PPO、DDPG、D4PG 等
- 構建一個使用強化學習方法訓練的實用硬體機器人，成本低於 100 美元
- 探索微軟的 TextWorld 環境，這是一個互動小說遊戲平台
- 在強化學習中使用離散優化來解決魔術方塊
- 教導您的代理使用 AlphaGo Zero 玩 Connect 4
- 探索最新的深度強化學習研究，主題包括 AI 聊天機器人
- 發現高級探索技術，包括噪聲網絡和網絡蒸餾技術
- 暢銷的深度強化學習入門書籍第二版，新增六個章節
- 學習高級探索技術，包括噪聲網絡、偽計數和網絡蒸餾方法
- 將強化學習方法應用於廉價的硬體機器人平台

作者簡介

Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lies from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data, machine learning, and large parallel distributed HPC and non-HPC systems, he is able to explain a number of complicated concepts in simple words and vivid examples. His current areas of interest are in practical applications of deep learning, such as deep natural language processing and deep reinforcement learning. Maxim lives in Moscow, Russian Federation, with his family.

作者簡介(中文翻譯)

Maxim Lapan 是一位深度學習愛好者和獨立研究員。他擁有 15 年的軟體開發和系統架構師的工作經驗，背景涵蓋從低階 Linux 核心驅動程式開發到性能優化及設計在數千台伺服器上運行的分散式應用程式。憑藉在大數據、機器學習以及大型平行分散式高效能計算（HPC）和非 HPC 系統方面的豐富工作經驗，他能夠用簡單的語言和生動的例子解釋許多複雜的概念。他目前的興趣領域包括深度學習的實際應用，例如深度自然語言處理和深度強化學習。Maxim 與家人居住在俄羅斯聯邦的莫斯科。

目錄大綱

  1. What Is Reinforcement Learning?
  2. OpenAI Gym
  3. Deep Learning with PyTorch
  4. The Cross-Entropy Method
  5. Tabular Learning and the Bellman Equation
  6. Deep Q-Networks
  7. Higher-Level RL libraries
  8. DQN Extensions
  9. Ways to Speed up RL
  10. Stocks Trading Using RL
  11. Policy Gradients – an Alternative
  12. The Actor-Critic Method
  13. Asynchronous Advantage Actor-Critic
  14. Training Chatbots with RL
  15. The TextWorld environment
  16. Web Navigation
  17. Continuous Action Space
  18. RL in Robotics
  19. Trust Regions – PPO, TRPO, ACKTR, and SAC
  20. Black-Box Optimization in RL
  21. Advanced exploration
  22. Beyond Model-Free – Imagination
  23. AlphaGo Zero
  24. RL in Discrete Optimisation
  25. Multi-agent RL

目錄大綱(中文翻譯)


  1. What Is Reinforcement Learning?

  2. OpenAI Gym

  3. Deep Learning with PyTorch

  4. The Cross-Entropy Method

  5. Tabular Learning and the Bellman Equation

  6. Deep Q-Networks

  7. Higher-Level RL libraries

  8. DQN Extensions

  9. Ways to Speed up RL

  10. Stocks Trading Using RL

  11. Policy Gradients – an Alternative

  12. The Actor-Critic Method

  13. Asynchronous Advantage Actor-Critic

  14. Training Chatbots with RL

  15. The TextWorld environment

  16. Web Navigation

  17. Continuous Action Space

  18. RL in Robotics

  19. Trust Regions – PPO, TRPO, ACKTR, and SAC

  20. Black-Box Optimization in RL

  21. Advanced exploration

  22. Beyond Model-Free – Imagination

  23. AlphaGo Zero

  24. RL in Discrete Optimisation

  25. Multi-agent RL

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