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Deep Reinforcement Learning in Python: A Hands-On Introduction

Author: Laura Graesser Wah Loon Keng

SKU: 9780135172384 Category:
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Description

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. Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes: Components of an RL system, including environment and agents Value-based algorithms: SARSA, Q-learning and extensions, offline learning Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques Combined methods: Actor-Critic and extensions; scalability through async methods Agent evaluation Advanced and experimental techniques, and more show more

Additional information

Weight 650 g
Author

Laura Graesser Wah Loon Keng

Publisher

Pearson Education (US)

Binding

Paperback / softback

ISBN-10

0135172381

Dimensions

231 x 168 x 13

Language

English

Country of Pub

United States

Book Condition

New

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