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Foundations of Deep Reinforcement Learning

Theory and Practice in Python

Laura Graesser, Wah Loon Keng
Livre broché | Anglais | Addison-Wesley Data & Analytics
53,45 €
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Description

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice

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.

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.

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Contenu

Nombre de pages :
416
Langue:
Anglais
Collection :

Caractéristiques

EAN:
9780135172384
Date de parution :
05-12-19
Format:
Livre broché
Format numérique:
Trade paperback (VS)
Dimensions :
175 mm x 229 mm
Poids :
498 g

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