Foundations of Deep Reinforcement Learning (Addison-Wesley)
Wednesday, 30 September 2020

In the past decade deep reinforcement learning (RL) has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics. This book, subtitled "Theory and Practice in Python", is an introduction to deep RL that combines both theory and implementation. Laura Graesser and Wah Loon Keng start with intuition, then explain the theory of deep RL algorithms, discusse implementations in its companion software library SLM Lab, and finish with the practical details of getting deep RL to work.

<ASIN:0135172381>

 

Author: Laura Graesser and Wah Loon Keng
Publisher: Addison-Wesley
Date: December 2019
Pages: 416
ISBN: 978-0135172384
Print: 0135172381
Kindle: B07ZVYZC6F
Audience: Developers interested in reinforcement learning
Level: Intermediate/Advanced
Category: Artificial Intelligence

 

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

For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

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