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

$10.00

Author Laura Graesser, Wah Loon Keng
Edition 1
Edition Year 2019
Format PDF
ISBN 9780135172384
Language English
Number Of Pages 416
Publisher Addison-Wesley Professional

Description

Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.
This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.
  • 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
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep 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.

Additional information

Author

Laura Graesser, Wah Loon Keng

Edition

1

Edition Year

2019

Format

PDF

ISBN

9780135172384

Language

English

Number Of Pages

416

Publisher

Addison-Wesley Professional

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