Foundations of Deep Reinforcement LearningThe 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.
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This book is good for graduate students who are beginners in deep reinforcement learning and want to grasp both the practical and theoretical understanding of deep reinforcement learning. Unlike other books, this book is laced with practical illustration of mathematical equations and how to implement the algorithms with software programming. It is also a refresher book for experts in the field.
Contents
PolicyBased and ValueBased Algorithms | |
SARSA | |
Deep QNetworks DQN | |
Improving | |
Combined Methods | |
Parallelization Methods | |
Hardware | |
Environment Design | |
Actions | |
Rewards | |
Transition Function | |
Epilogue | |
B Example Environments | |
References | |
Other editions - View all
Foundations of Deep Reinforcement Learning: Theory and Practice in Python Laura Graesser,Wah Loon Keng No preview available - 2020 |
Common terms and phrases
action advantage agent algorithm applied Atari batch calculate changes Chapter Click combined complex components consider contains continuous debugging deep RL discrete discussed distribution effective elements environment episode Equation estimate example experiences exploration Figure four frame fully function given global human hyperparameters implementation import Initialize input known layers look loss means memory method moving multiple neural network objective observed OpenAI optimal output parallelization parameters performance pixels play policy gradient position possible practice probability problem produce provides Q-value Reinforcement Learning Replay represent requires reward sampling SARSA shown shows signal simple single SLM Lab solve space spec file specific standard step stored task tensor transition trial typically unit update values view code image
Bibliographic information
| Title | Foundations of Deep Reinforcement Learning Addison-Wesley Data & Analytics Series |
| Authors | Laura Graesser, Wah Loon Keng |
| Publisher | Addison-Wesley Professional, 2019 |
| ISBN | 0135172489, 9780135172483 |
| Length | 416 pages |
| Subjects | › › Computers / Artificial Intelligence / General Computers / Data Science / Data Analytics |
| Export Citation | BiBTeX EndNote RefMan |