Description
| Product ID: | 9780135172384 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | US |
| Series: | Addison-Wesley Data & Analytics Series |
| Title: | Foundations of Deep Reinforcement Learning |
| Subtitle: | Theory and Practice in Python |
| Authors: | Author: Laura Graesser, Wah Loon Keng |
| Page Count: | 416 |
| Subjects: | Databases, Databases, Artificial intelligence, Artificial intelligence |
| Description: | 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. 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.
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. 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:
The accessible, hands-on, full-color tutorial for building practical deep reinforcement learning solutions
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| Imprint Name: | Addison Wesley |
| Publisher Name: | Pearson Education (US) |
| Country of Publication: | GB |
| Publishing Date: | 2020-02-04 |
