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

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)

《深度強化學習實戰 第二版》是暢銷指南的更新擴充版本，介紹了最新的強化學習（RL）工具和技術。本書向讀者介紹了RL的基礎知識，並提供了編寫智能學習代理以執行各種實際任務的實踐能力。

本書新增了六章，涵蓋了RL領域的各種最新發展，包括離散優化（解決魔術方塊問題）、多智能體方法、微軟的TextWorld環境、高級探索技術等等。通過閱讀本書，您將對這一新興領域的最新創新有深入的了解。

此外，您還將獲得有關深度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的實用見解。您還將發現如何以不到100美元的成本建立一個使用RL訓練的真實硬件機器人，以及如何在只需30分鐘的訓練時間內使用逐步代碼優化解決乒乓球環境的方法。

總之，《深度強化學習實戰 第二版》是您在探索RL的激動人心的複雜性時的良師益友，通過真實世界的例子幫助您獲得實踐經驗和知識。

本書的主要內容包括：
- 理解RL的深度學習背景，並實現複雜的深度學習模型
- 評估包括交叉熵、DQN、演員-評論家、TRPO、PPO、DDPG、D4PG等在內的RL方法
- 以不到100美元的成本建立一個使用RL方法訓練的實用硬件機器人
- 探索微軟的TextWorld環境，這是一個互動小說遊戲平台
- 使用RL中的離散優化方法解決魔術方塊問題
- 教您的代理使用AlphaGo Zero玩連連看
- 探索包括AI聊天機器人在內的最新深度RL研究
- 發現高級探索技術，包括噪聲網絡和網絡蒸餾技術
- 第二版是暢銷的深度強化學習入門書籍，新增了六章
- 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RL方法應用於廉價硬件機器人平台

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者。他擁有15年的軟體開發和系統架構的工作經驗，從低階的Linux核心驅動程式開發到性能優化和設計分佈式應用程式，這些應用程式在數千台伺服器上運行。他在大數據、機器學習和大型並行分佈式高性能計算和非高性能計算系統方面擁有豐富的工作經驗，能夠用簡單的詞語和生動的例子解釋一些複雜的概念。他目前的研究興趣在於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與他的家人居住在俄羅斯莫斯科。

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)

- 什麼是強化學習？
- OpenAI Gym
- 使用 PyTorch 進行深度學習
- 交叉熵方法
- 表格學習和貝爾曼方程
- 深度 Q 網絡
- 更高級的強化學習庫
- DQN 擴展
- 加速強化學習的方法
- 使用強化學習進行股票交易
- 策略梯度 - 另一種方法
- 演員-評論家方法
- 非同步優勢演員-評論家
- 使用強化學習訓練聊天機器人
- TextWorld 環境
- 網頁導航
- 連續動作空間
- 強化學習在機器人領域的應用
- 信任區域 - PPO、TRPO、ACKTR 和 SAC
- 強化學習中的黑盒優化
- 進階探索
- 超越無模型 - 想像力
- AlphaGo Zero
- 強化學習在離散優化中的應用
- 多智能體強化學習

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