Verwandte Artikel zu Foundations of Deep Reinforcement Learning: Theory...
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) - Softcover
Inhaltsangabe
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.
- Understand each key aspect of a deep RL problem
- Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
- Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
- Understand how algorithms can be parallelized synchronously and asynchronously
- Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
- Explore algorithm benchmark results with tuned hyperparameters
- Understand how deep RL environments are designed
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Über die Autorin bzw. den Autor
Laura Graesser is a research software engineer working in robotics at Google. She holds a master’s degree in computer science from New York University, where she specialized in machine learning.
Wah Loon Keng is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Neu kaufen
Diesen Artikel anzeigen
EUR 39,82
EUR 5,71 für den Versand von Vereinigtes Königreich nach Deutschland
Versandziele, Kosten & DauerSuchergebnisse für Foundations of Deep Reinforcement Learning: Theory...
Beispielbild für diese ISBN
Foundations of Deep Reinforcement Learning: Theory and Practice in Python
Verlag:
Addison-Wesley Professional, 2019
ISBN 10: 0135172381
ISBN 13: 9780135172384
Gebraucht
Paperback
Anbieter: ThriftBooks-Dallas, Dallas, TX, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.01. Bestandsnummer des Verkäufers G0135172381I4N00
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Verlag:
Addison-Wesley Professional (edition 1), 2019
ISBN 10: 0135172381
ISBN 13: 9780135172384
Gebraucht
Paperback
Anbieter: BooksRun, Philadelphia, PA, USA
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Bestandsnummer des Verkäufers 0135172381-8-1
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
DEEP REINFORCEMENT LEARNING IN PYTHON
Anbieter: Speedyhen, London, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: NEW. Bestandsnummer des Verkäufers NW9780135172384
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Deep Reinforcement Learning in Python: A Hands-On Introduction
Verlag:
Pearson Education (US), 2019
ISBN 10: 0135172381
ISBN 13: 9780135172384
Neu
Paperback / softback
Anbieter: THE SAINT BOOKSTORE, Southport, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Paperback / softback. Zustand: New. New copy - Usually dispatched within 2 working days. 730. Bestandsnummer des Verkäufers B9780135172384
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Foundations of Deep Reinforcement Learning
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Bestandsnummer des Verkäufers GB-9780135172384
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Foundations of Deep Reinforcement Learning
Anbieter: PBShop.store US, Wood Dale, IL, USA
Verkäuferbewertung 5 von 5 Sternen
PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000. Bestandsnummer des Verkäufers GB-9780135172384
Anzahl: 2 verfügbar
Foto des Verkäufers
Foundations of Deep Reinforcement Learning
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Paperback. Zustand: New. 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. Understand each key aspect of a deep RL problemExplore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)Understand how algorithms can be parallelized synchronously and asynchronouslyRun algorithms in SLM Lab and learn the practical implementation details for getting deep RL to workExplore algorithm benchmark results with tuned hyperparametersUnderstand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. Bestandsnummer des Verkäufers LU-9780135172384
Anzahl: 2 verfügbar
Foto des Verkäufers
Deep Reinforcement Learning in Python
Anbieter: moluna, Greven, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Kartoniert / Broschiert. Zustand: New. Über den AutorLaura Graesser is a research software engineer working in robotics at Google. She holds a master s degree in computer science from New York University, where she specialized in machine learning. Bestandsnummer des Verkäufers 237325859
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9780135172384_new
Anzahl: 2 verfügbar
Foto des Verkäufers
Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. 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. Bestandsnummer des Verkäufers 9780135172384
Anzahl: 1 verfügbar