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Foundations of deep reinforcement learning : theory and practice in Python

Foundations of deep reinforcement learning : theory and practice in Python (Loan 3 times)

Material type
단행본
Personal Author
Graesser, Laura. Keng, Wah Loon.
Title Statement
Foundations of deep reinforcement learning : theory and practice in Python / Laura Graesser, Wah Loon Keng.
Publication, Distribution, etc
Boston :   Addison-Wesley,   c2020.  
Physical Medium
xxiv, 379 p. : col. ill. ; 24 cm.
Series Statement
Addison Wesley data & analytics series
ISBN
9780135172384 0135172381
요약
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. 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 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.
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Reinforcement learning. Python (Computer program language).
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001 000046021481
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008 200320s2020 maua b 001 0 eng d
020 ▼a 9780135172384
020 ▼a 0135172381
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.31 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b G735f
100 1 ▼a Graesser, Laura.
245 1 0 ▼a Foundations of deep reinforcement learning : ▼b theory and practice in Python / ▼c Laura Graesser, Wah Loon Keng.
260 ▼a Boston : ▼b Addison-Wesley, ▼c c2020.
300 ▼a xxiv, 379 p. : ▼b col. ill. ; ▼c 24 cm.
490 1 ▼a Addison Wesley data & analytics series
504 ▼a Includes bibliographical references and index.
520 ▼a 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. 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 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.
650 0 ▼a Reinforcement learning.
650 0 ▼a Python (Computer program language).
700 1 ▼a Keng, Wah Loon.
830 0 ▼a Addison-Wesley data and analytics series.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 G735f Accession No. 121252739 Availability In loan Due Date 2021-08-25 Make a Reservation Available for Reserve R Service M

Contents information

Table of Contents

Chapter 1: Introduction to Reinforcement Learning
Part I: Policy-Based and Value-Based Algorithms
Chapter 2: Policy Gradient
Chapter 3: State Action Reward State Action
Chapter 4: Deep Q-Networks
Chapter 5: Improving Deep Q-Networks
Part II: Combined Methods
Chapter 6: Advantage Actor-Critic
Chapter 7: Proximal Policy Optimization
Chapter 8: Parallelization Methods
Chapter 9: Algorithm Summary
Part III: Practical Tips
Chapter 10: Getting Reinforcement Learning to Work
Chapter 11: SLM Lab
Chapter 12: Network Architectures
Chapter 13: Hardward
Chapter 14: Environment Design
Epilogue
Appendix A: Deep Reinforcement Learning Timeline
Appendix B: Example Environments
References
Index

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