Foundations of Deep Reinforcement Learning

Laura Graesser, Wah Loon Keng

ISBN 10: 0135172381 / ISBN 13: 9780135172384

Editore: Pearson Education (US), US, 2020

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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.

Codice articolo LU-9780135172384

Dati bibliografici

• Titolo: Foundations of Deep Reinforcement Learning
• Editore: Pearson Education (US), US
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 0135172381
• ISBN 13: 9780135172384
• Rilegatura: Paperback
• Condizione: New
• Peso: 600 grammi

Descrizione articolo

Riassunto

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

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Informazioni sull?autore

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.