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 PythonKey FeaturesEnter 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 artifici...
Paperback: 318 pages
Publisher: Packt Publishing (June 28, 2018)
Language: English
ISBN-10: 1788836529
ISBN-13: 978-1788836524
Product Dimensions: 7.5 x 0.7 x 9.2 inches
Amazon Rank: 161936
Format: PDF Text TXT book
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“reinforcement learning made simple. Simple solid math when needed, with good python code.Solid introduction to reinforcement learning traditional strategies and modern deep reinforcement learning.Definitively recommend....”
l intelligence (AI). Hands-On Reinforcement Learning with Python will help you master not only basic reinforcement learning algorithms but also advanced deep reinforcement learning (DRL) 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 this 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 RL methods, algorithms, and elementsTrain an agent to walk using OpenAI Gym and TensorflowUnderstand Markov decision process, Bellman's optimality, and temporal difference (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 ForHands-On Reinforcement Learning with Python is for machine learning developers and deep learning enthusiasts interested in artificial intelligence and want to learn about reinforcement learning from scratch. 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 DQN
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