000			03219cam a2200301zu 4500
001			88901560
003			FRCYB88901560
005			20250107233326.0
006			m o d
007			cr un
008			250108s2020 fr | o|||||0|0|||eng d
020			_a9781800200456
035			_aFRCYB88901560
040			_aFR-PaCSA _ben _c _erda
100	1		_aPalmas, Alessandro
245	0	1	_aThe Reinforcement Learning Workshop _c['Palmas, Alessandro', 'Ghelfi, Emanuele', 'Petre, Dr. Alexandra Galina']
264		1	_bPackt Publishing _c2020
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aPalmas, Alessandro
700	0		_aGhelfi, Emanuele
700	0		_aPetre, Dr. Alexandra Galina
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88901560 _qtext/html _a
520			_aStart 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 Key Features Use TensorFlow to write reinforcement learning agents for performing challenging tasks Learn how to solve finite Markov decision problems Train models to understand popular video games like Breakout Book Description 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. What you will learn Use OpenAI Gym as a framework to implement RL environments Find out how to define and implement reward function Explore Markov chain, Markov decision process, and the Bellman equation Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning Understand the multi-armed bandit problem and explore various strategies to solve it Build a deep Q model network for playing the video game Breakout Who this book is for 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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