The reinforcement learning workshop: learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems
bStart with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide/b h4Key Features/h4 ul liUse TensorFlow to write reinforcement learning agents for performing challenging tasks/l...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2020
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| Schlagworte: | |
| Online-Zugang: | BTW01 |
| Zusammenfassung: | bStart with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide/b h4Key Features/h4 ul liUse TensorFlow to write reinforcement learning agents for performing challenging tasks/li liLearn how to solve finite Markov decision problems/li liTrain models to understand popular video games like Breakout/li /ul h4Book Description/h4 Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. h4What you will learn/h4 ul liUse OpenAI Gym as a framework to implement RL environments/li liFind out how to define and implement reward function/li liExplore Markov chain, Markov decision process, and the Bellman equation/li liDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning/li liUnderstand the multi-armed bandit problem and explore various strategies to solve it/li liBuild a deep Q model network for playing the video game Breakout/li /ul h4Who this book is for/h4 If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary |
| Beschreibung: | 1 Online-Ressource (822 Seiten) |
| ISBN: | 9781800209961 |
Internformat
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| 245 | 1 | 0 | |a The reinforcement learning workshop |b learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems |c Alessandro Palmas |
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| 520 | |a bStart with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide/b h4Key Features/h4 ul liUse TensorFlow to write reinforcement learning agents for performing challenging tasks/li liLearn how to solve finite Markov decision problems/li liTrain models to understand popular video games like Breakout/li /ul h4Book Description/h4 Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. | ||
| 520 | |a You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. | ||
| 520 | |a h4What you will learn/h4 ul liUse OpenAI Gym as a framework to implement RL environments/li liFind out how to define and implement reward function/li liExplore Markov chain, Markov decision process, and the Bellman equation/li liDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning/li liUnderstand the multi-armed bandit problem and explore various strategies to solve it/li liBuild a deep Q model network for playing the video game Breakout/li /ul h4Who this book is for/h4 If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary | ||
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Datensatz im Suchindex
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| id | DE-604.BV047070142 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:13:34Z |
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| institution | BVB |
| isbn | 9781800209961 |
| language | English |
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| spelling | Palmas, Alessandro Verfasser aut The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems Alessandro Palmas Birmingham Packt Publishing Limited 2020 1 Online-Ressource (822 Seiten) txt rdacontent c rdamedia cr rdacarrier bStart with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide/b h4Key Features/h4 ul liUse TensorFlow to write reinforcement learning agents for performing challenging tasks/li liLearn how to solve finite Markov decision problems/li liTrain models to understand popular video games like Breakout/li /ul h4Book Description/h4 Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. h4What you will learn/h4 ul liUse OpenAI Gym as a framework to implement RL environments/li liFind out how to define and implement reward function/li liExplore Markov chain, Markov decision process, and the Bellman equation/li liDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning/li liUnderstand the multi-armed bandit problem and explore various strategies to solve it/li liBuild a deep Q model network for playing the video game Breakout/li /ul h4Who this book is for/h4 If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks Ghelfi, Emanuele Sonstige oth Petre, Dr. Alexandra Galina Sonstige oth Kulkarni, Mayur Sonstige oth Erscheint auch als Druck-Ausgabe 9781800200456 |
| spellingShingle | Palmas, Alessandro The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| title | The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems |
| title_auth | The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems |
| title_exact_search | The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems |
| title_exact_search_txtP | The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems |
| title_full | The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems Alessandro Palmas |
| title_fullStr | The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems Alessandro Palmas |
| title_full_unstemmed | The reinforcement learning workshop learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems Alessandro Palmas |
| title_short | The reinforcement learning workshop |
| title_sort | the reinforcement learning workshop learn how to apply cutting edge reinforcement learning algorithms to a wide range of control problems |
| title_sub | learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems |
| topic | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
| topic_facet | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Neural Networks |
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