Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Paperback)
暫譯: 深度強化學習基礎：Python中的理論與實踐（平裝本）

Graesser, Laura, Keng, Wah Loon

Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Paperback)

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商品描述

In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence.
Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes:

  • Components of an RL system, including environment and agents
  • Value-based algorithms: SARSA, Q-learning and extensions, offline learning
  • Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques
  • Combined methods: Actor-Critic and extensions; scalability through async methods
  • Agent evaluation
  • Advanced and experimental techniques, and more

商品描述(中文翻譯)

在短短幾年內，深度強化學習（Deep Reinforcement Learning, DRL）系統如 DeepMind 的 DQN 已經取得了顯著的成果。這種混合的機器學習方法與人類學習有許多相似之處：它的無監督自我學習、自我發現策略、使用記憶、探索與利用的平衡，以及其卓越的靈活性。DRL 本身令人興奮，可能預示著在通用人工智慧方面會有更顯著的進展。

Python 中的深度強化學習：實作入門 是開始學習 DRL 最快速且最容易的方法。作者透過實際的操作範例，使用他們先進的 OpenAI Lab 框架進行教學。在提供堅實的理論概述的同時，他們強調建立對理論的直覺，而不是對結果進行深入的數學處理。內容涵蓋：

- 強化學習系統的組成部分，包括環境和代理
- 基於價值的演算法：SARSA、Q-learning 及其擴展、離線學習
- 基於策略的演算法：REINFORCE 及其擴展；與基於價值的技術比較
- 結合方法：Actor-Critic 及其擴展；透過非同步方法的可擴展性
- 代理評估
- 進階和實驗技術，等等

作者簡介

Laura Graesser is a research software engineer working in robotics at Google. She holds a master's degree in computer science from New York University, where she specialized in machine learning.

Wah Loon Keng is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science.

作者簡介(中文翻譯)

Laura Graesser 是一位在 Google 從事機器人技術的研究軟體工程師。她擁有紐約大學的計算機科學碩士學位，專攻機器學習。

Wah Loon Keng 是 Machine Zone 的 AI 工程師，專注於將深度強化學習應用於工業問題。他擁有理論物理和計算機科學的背景。

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