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Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python
Key FeaturesYour 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 DescriptionReinforcement 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 learnUnderstand 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 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.
Table of ContentsIntroduction to Reinforcement LearningGetting started with OpenAI and TensorflowMarkov Decision process and Dynamic ProgrammingGaming with Monte Carlo Tree SearchTemporal Difference LearningMulti-Armed Bandit ProblemDeep Learning FundamentalsDeep Learning and ReinforcementPlaying Doom With Deep Recurrent Q NetworkAsynchronous Advantage Actor Critic NetworkPolicy Gradients and OptimizationCapstone Project – Car Racing using DQNCurrent Research and Next Steps
Key FeaturesYour 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 DescriptionReinforcement 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 learnUnderstand 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 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.
Table of ContentsIntroduction to Reinforcement LearningGetting started with OpenAI and TensorflowMarkov Decision process and Dynamic ProgrammingGaming with Monte Carlo Tree SearchTemporal Difference LearningMulti-Armed Bandit ProblemDeep Learning FundamentalsDeep Learning and ReinforcementPlaying Doom With Deep Recurrent Q NetworkAsynchronous Advantage Actor Critic NetworkPolicy Gradients and OptimizationCapstone Project – Car Racing using DQNCurrent Research and Next Steps
- GenresArtificial Intelligence
320 pages, Kindle Edition
Published June 28, 2018
21 people are currently reading
37 people want to read
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Displaying 1 - 3 of 3 reviews
April 4, 2019
Nice read
Book is good for the basics of reinforcement learning , I would like to give the feedback that in next edition make the formulae and image properly visible in PDF and Kindle format.
Book is good for the basics of reinforcement learning , I would like to give the feedback that in next edition make the formulae and image properly visible in PDF and Kindle format.
March 5, 2020
Looking forward to read the book
November 26, 2023
Some math explanation is skin deep which is targeting easy-readers but not for practioners.
Displaying 1 - 3 of 3 reviews