Deep reinforcement learning hands-on : Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo zero and more Maxim Lapan.

By:

Lapan, Maxim

Material type: Text

TextPublication details: Birmingham-Mumbai: Packt Publishing, 2018.Edition: 1 EdDescription: i-xvi+523pISBN:

9781788834247

Subject(s):

Reinforcement learning. Machine learning. Natural language processing (Computer science)

DDC classification:

006.31 LAP-D

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.

Summary: 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 methods to training your agent to beat arcade ...

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Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics Processing Center	Dept. of Computational Biology and Bioinformatics	006.31 LAP-D (Browse shelf(Opens below))	Available		DCB3872

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Previous	006.31 GOP-A Applied machine learning	006.31 HAS.E The elements of statistical learning: Data Mining, Inference, and Prediction	006.31 KRO-D Deep learning illustrated : a visual, interactive guide to artificial intelligence	006.31 LAP-D Deep reinforcement learning hands-on : Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo zero and more	006.31 MIT-M Machine Learning	006.31 MOR-A AI and machine learning for coders : a Programmer's guide to artificial intelligence	006.31 MOR-A AI and Machine Learning for on-Device Development : a Programmer's guide	Next

"Expert insight."

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.

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 methods to training your agent to beat arcade ...

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