Foundations of Deep Reinforcement Learning : Theory and Practice in Python

By:

Graesser, Laura

By:

Keng, Wah Loon

Publication details: Noida: Pearson India Education Services Pvt. Ltd., 2022Description: 379ISBN:

9789390394852

Subject(s):

DDC classification:

006.31 GRA

Summary: 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.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Reference Book	Alliance School of Advanced Computing	Computer Science and Information Technology	006.31 GRA (Browse shelf(Opens below))	Not for loan		E16310

Total holds: 0

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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.

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