Hands on Reinforcement Learning with Python (29 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 1.21. Book.

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Digital. Condition: New. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with PythonAbout This Book. Your entry point into the world of artificial intelligence using the power of Python. An example-rich guide to master various RL and DRL algorithms. Explore various state-of-the-art architectures along with mathWho This Book Is ForIf 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.What You Will Learn. Understand the basics of reinforcement learning methods, algorithms, and elements. Train an agent to walk using OpenAI Gym and Tensorflow. Understand the Markov Decision Process, Bellman's optimality, and TD learning. Solve multi-armed-bandit problems using various algorithms. Master deep learning algorithms, such as RNN, LSTM, and CNN with applications. Build intelligent agents using the DRQN algorithm to play the Doom game. Teach agents to play the Lunar Lander game using DDPG. Train an agent to win a car racing game using dueling DQNIn DetailReinforcement 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.Style and approachThis is a hands-on book designed to further expand your machine learning skills by understanding reinforcement to deep reinforcement learning algorithms with applications in Python.

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Condition: New. Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.&Uumlber den Autor.

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Digital. Condition: New. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with PythonAbout This Book. Your entry point into the world of artificial intelligence using the power of Python. An example-rich guide to master various RL and DRL algorithms. Explore various state-of-the-art architectures along with mathWho This Book Is ForIf 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.What You Will Learn. Understand the basics of reinforcement learning methods, algorithms, and elements. Train an agent to walk using OpenAI Gym and Tensorflow. Understand the Markov Decision Process, Bellman's optimality, and TD learning. Solve multi-armed-bandit problems using various algorithms. Master deep learning algorithms, such as RNN, LSTM, and CNN with applications. Build intelligent agents using the DRQN algorithm to play the Doom game. Teach agents to play the Lunar Lander game using DDPG. Train an agent to win a car racing game using dueling DQNIn DetailReinforcement 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.Style and approachThis is a hands-on book designed to further expand your machine learning skills by understanding reinforcement to deep reinforcement learning algorithms with applications in Python.

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Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 222.

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.