Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series). Questo articolo non è disponibile.
Graesser, Laura; Keng, Wah Loon
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Lingua: inglese
Editore: Addison-Wesley Professional, 2019
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In English.
Codice articolo ria9780135172384_new
- Titolo
- Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
- Autore
- Graesser, Laura; Keng, Wah Loon
- Editore
- Addison-Wesley Professional
- Anno di pubblicazione
- 2019
- Condizione
- New
- Rilegatura
- Brossura
- Lingua
- inglese
- ISBN 10
- 0135172381
- ISBN 13
- 9780135172384
- Peso dell'articolo
- 748 grammi
- Serie
- Libro 8 di 14: Addison-Wesley Data & Analytics
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 gamessuch as Go, Atari games, and DotA 2to robotics.
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 gamessuch as Go, Atari games, and DotA 2to 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
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Informazioni sull’autore
Laura Graesser is a research software engineer working in robotics at Google. She holds a masters 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.
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