Foundations of Deep Reinforcement Learning : Theory and Practice in Python

Laura Graesser

ISBN 10: 0135172381 / ISBN 13: 9780135172384

Published by Pearson Education Dez 2019, 2019

About this Item

Neuware - In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence. Seller Inventory # 9780135172384

Bibliographic Details

Title: Foundations of Deep Reinforcement Learning :...

Publisher: Pearson Education Dez 2019

Publication Date: 2019

Binding: Taschenbuch

Condition: Neu

About this title

Synopsis:

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.

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