Deep Reinforcement Learning Python Master by Sudharsan Ravichandiran (18 results)
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Paperback or Softback. Condition: New. Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow. Book.
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Python Intensive Learning Practice: Applying OpenAI Gym and TensorFlow to Master Reinforcement Learning and Deep Reinforcement Learning(Chinese Edition)
[ YIN DU ] SU DA SANG LA WEI SHANG DI LAN ( Sudharsan . Ravichandiran ) ZHU
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paperback. Condition: New. Paperback. Pub Date: 2019-01-01 Pages: 203 Language: Chinese Publisher: Mechanical Industry Press Reinforcement learning is an important machine learning method. and has many applications in the fields of agent and analysis and prediction. Python Intensive Learning Practice: Applying OpenAI Gym and Ten…sorFlow to Master Reinforcement Learning and.
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Taschenbuch. Condition: Neu. Hands-On Reinforcement Learning with Python | Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow | Sudharsan Ravichandiran | Taschenbuch | Kartoniert / Broschiert | Englisch | 2018 | Packt Publishing | EAN 9781788836524 | Verantwortliche Person für die EU: Libri GmbH…, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A hands-on guide enriched with examples to master deep reinforcement learning algorithms with PythonKey Features:Your entry point into the world of artificial intelligence using the power of PythonAn example-rich guide to master various…RL and DRL algorithmsExplore various state-of-the-art architectures along with mathBook Description:Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.What You Will Learn:Understand the basics of reinforcement learning methods, algorithms, and elementsTrain an agent to walk using OpenAI Gym and TensorflowUnderstand the Markov Decision Process, Bellman's optimality, and TD learningSolve multi-armed-bandit problems using various algorithmsMaster deep learning algorithms, such as RNN, LSTM, and CNN with applicationsBuild intelligent agents using the DRQN algorithm to play the Doom gameTeach agents to play the Lunar Lander game using DDPGTrain an agent to win a car racing game using dueling DQNWho this book is for:If you're a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
