Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more /

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This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging problems than ever before. Apply deep RL met...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Lapan, Maxim (Author)
Format: Electronic eBook
Language:Inglés
Published: Birmingham, UK : Packt Publishing, 2018.
Subjects:
Reinforcement learning.
Machine learning.
Natural language processing (Computer science)
Artificial intelligence.
Natural Language Processing
Artificial Intelligence
Machine Learning
Apprentissage par renforcement (Intelligence artificielle)
Apprentissage automatique.
Traitement automatique des langues naturelles.
Intelligence artificielle.
artificial intelligence.
COMPUTERS > General.
Artificial intelligence
Machine learning
Reinforcement learning
Online Access:Texto completo (Requiere registro previo con correo institucional)
Table of Contents:
  • Table of ContentsWhat is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksDQN ExtensionsStocks Trading Using RLPolicy Gradients
  • An AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticChatbots Training with RL Web NavigationContinuous Action SpaceTrust Regions
  • TRPO, PPO, and ACKTRBlack-Box Optimization in RLBeyond Model-Free
  • ImaginationAlphaGo Zero.

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