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

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（DRL）系統，如DeepMind的DQN，已經取得了令人驚人的成果。這種混合式機器學習方法與人類學習有許多相似之處：它是無監督的自我學習，自我發現策略，使用記憶，平衡探索和利用，以及其卓越的靈活性。DRL本身就非常令人興奮，它可能預示著在通用人工智能方面更加驚人的進展。

《Python深度強化學習：實戰入門》是開始學習DRL的最快、最易於理解的方式。作者通過他們先進的OpenAI Lab框架提供實用的實例教學。在提供堅實的理論概述的同時，他們強調對理論建立直觀感受，而不是深入的數學處理結果。內容包括：

- RL系統的組件，包括環境和代理
- 基於價值的算法：SARSA、Q-learning和擴展、離線學習
- 基於策略的算法：REINFORCE和擴展；與基於價值的技術的比較
- 綜合方法：Actor-Critic和擴展；通過異步方法實現可擴展性
- 代理評估
- 高級和實驗性技術等等

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Google的研究軟體工程師，專注於機器人領域。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，他將深度強化學習應用於工業問題。他在理論物理學和計算機科學方面都有背景。

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