Reinforcement Learning Algorithms with Python : Learn, understand, and develop smart algorithms for addressing AI challenges (Paperback)

Lonza, Andrea

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商品描述

Key Features

  • Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks
  • Understand and develop model-free and model-based algorithms for building self-learning agents
  • Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategies

Book Description

Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents.

Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS.

By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.

What you will learn

  • Develop an agent to play CartPole using the OpenAI Gym interface
  • Discover the model-based reinforcement learning paradigm
  • Solve the Frozen Lake problem with dynamic programming
  • Explore Q-learning and SARSA with a view to playing a taxi game
  • Apply Deep Q-Networks (DQNs) to Atari games using Gym
  • Study policy gradient algorithms, including Actor-Critic and REINFORCE
  • Understand and apply PPO and TRPO in continuous locomotion environments
  • Get to grips with evolution strategies for solving the lunar lander problem

Who this book is for

If you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You'll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.

商品描述(中文翻譯)

主要特點


  • 學習、開發和部署高級強化學習算法以解決各種任務

  • 理解並開發無模型和有模型算法,用於構建自學習代理

  • 使用進階的強化學習概念和算法,如模仿學習和進化策略

書籍描述

強化學習(RL)是人工智慧中一個受歡迎且有前景的分支,涉及建立更智能的模型和代理,根據不斷變化的需求自動確定理想行為。本書將幫助您掌握RL算法並理解其實現,同時構建自學習代理。

從介紹在RL環境中工作所需的工具、庫和設置開始,本書涵蓋了RL的基礎知識,並深入探討了基於價值的方法,如Q學習和SARSA算法的應用。您將學習如何結合Q學習和神經網絡來解決複雜問題。此外,您還將研究策略梯度方法,如TRPO和PPO,以提高性能和穩定性,然後轉向DDPG和TD3確定性算法。本書還介紹了模仿學習技術的工作原理以及Dagger如何教會代理駕駛。您將了解進化策略和黑盒優化技術,並了解它們如何改進RL算法。最後,您將掌握UCB和UCB1等探索方法,並開發一種名為ESBAS的元算法。

通過閱讀本書,您將使用關鍵的RL算法來克服現實應用中的挑戰,並成為RL研究社區的一員。

您將學到什麼


  • 使用OpenAI Gym接口開發一個玩CartPole的代理

  • 探索基於模型的強化學習範式

  • 使用動態規劃解決Frozen Lake問題

  • 通過Q學習和SARSA玩出租車遊戲

  • 使用Gym將Deep Q-Networks(DQNs)應用於Atari遊戲

  • 研究策略梯度算法,包括Actor-Critic和REINFORCE

  • 理解並應用PPO和TRPO在連續運動環境中

  • 掌握進化策略以解決月球著陸器問題

適合閱讀對象

如果您是AI研究人員、深度學習使用者或任何想從頭學習強化學習的人,本書適合您。如果您想了解該領域的最新進展,也會發現這本強化學習書籍很有用。需要具備Python的工作知識。

作者簡介

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.

作者簡介(中文翻譯)

Andrea Lonza 是一位深度學習工程師,對人工智慧充滿熱情,並渴望創造出具有智能行為的機器。他通過學術和工業機器學習項目獲得了在強化學習、自然語言處理和計算機視覺方面的專業知識。他還參加了多個 Kaggle 競賽,取得了優異的成績。他總是在尋找有吸引力的挑戰,並喜歡證明自己的能力。

目錄大綱

  1. The Landscape of Reinforcement Learning
  2. Implementing RL Cycle and OpenAI Gym
  3. Solving Problems with Dynamic Programming
  4. Q learning and SARSA Applications
  5. Deep Q-Network
  6. Learning Stochastic and DDPG optimization
  7. TRPO and PPO implementation
  8. DDPG and TD3 Applications
  9. Model-Based RL
  10. Imitation Learning with the DAgger Algorithm
  11. Understanding Black-Box Optimization Algorithms
  12. Developing the ESBAS Algorithm
  13. Practical Implementation for Resolving RL Challenges

目錄大綱(中文翻譯)

- 強化學習的景觀
- 實現強化學習循環和 OpenAI Gym
- 使用動態規劃解決問題
- Q 學習和 SARSA 應用
- 深度 Q 網絡
- 學習隨機和 DDPG 優化
- TRPO 和 PPO 實現
- DDPG 和 TD3 應用
- 基於模型的強化學習
- 使用 DAgger 算法進行模仿學習
- 理解黑盒優化算法
- 開發 ESBAS 算法
- 解決強化學習挑戰的實際實施方法