Foundations of Deep Reinforcement Learning: Theory and Practice in Python – PDF

eBook details

  • Authors: Laura Graesser, Wah Loon Keng
  • File Size: 6 MB
  • Format: PDF
  • Length: 416 Pages
  • Publisher: Addison-Wesley Professional; 1st edition
  • Publication Date: November 20, 2019
  • Language: English
  • ISBN-10: 0135172381, 0135172489
  • ISBN-13: 9780135172384, 9780135172483


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Foundations of Deep Reinforcement Learning: Theory and Practice in Python – eBook PDF

The Present-day Introduction to Deep Reinforcement Learning that Combines Theory and Practice

Deep reinforcement learning (deep RL) integrates deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the last decade deep RL has attained remarkable results on a range of problems, from single and multiplayer games—such as Atari games, Go and DotA 2—to robotics.

Foundations of Deep Reinforcement Learning (PDF) is an introduction to deep RL that uniquely integrates both theory and implementation. It starts with intuition, then meticulously explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and ends with the practical details of getting deep RL to work. This guide is perfect for both computer science students and software engineers who are familiar with fundamental machine learning concepts and have a working understanding of Python.

  • Understand every key aspect of a deep RL problem
  • Understand how deep RL environments are designed
  • Explore algorithm benchmark results with tuned hyperparameters
  • Understand how algorithms can be parallelized synchronously and asynchronously
  • Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
  • Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
  • Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)


This ebook provides an accessible introduction to deep reinforcement learning encompassing the mathematical concepts behind popular algorithms along with their practical implementation. I think the ebook will be a valuable resource for anyone looking to implement deep reinforcement learning in practice.” Volodymyr Mnih, lead developer of DQN

An excellent book to quickly build expertise in the theory, language, and practical implementation of deep reinforcement learning algorithms. A clear exposition which uses familiar notation; all the latest techniques explained with concise, readable code, and not a page wasted in unrelated detours: it is the ideal way to develop a solid foundation on the topic.” Vincent Vanhoucke, principal scientist, Google

NOTE: The product only includes the ebook Foundations of Deep Reinforcement Learning: Theory and Practice in Python in PDF. No access codes are included.


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