Hands–On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow [Libérer E–pub]
Hands–On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow Libérer E–pub
The book is oorly written and difficult to follow The instructions in chapter 2 are messy and horribleDownload a sample before you buy I feel very disappointed with the contents of this book No explanation of the code No linkage among chapters No consistency with the coding style Website tutorials and youtube would be useful Very good examples and Sketchy Behavior plain delivery of knowledge Great for beginners Great book Extremely useful I got the chance to understand how to use RL with clear examples and a solid mathematical background that is suitably explained for Machine Learningractitioners Good overview of the topic but when curiosity lights up on specific technicalities the contents are too generic Too high level on most of the descriptions also available in the sparse opensource web too criptic on the big space dedicated to Tensorflow code Unfortunately still not found a A hands on guide enriched with examples to master deep reinforcement learning algorithms with PythonKey FeaturesYour entry Autumn Brides point into the world of artificial intelligence using theower 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 The Princess and the Three Knights 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 algorithmsThe 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 dynamicrogramming including value and olicy iteration This example rich guide will introd.
Ook on DN uite a oor book Lots of conceptual mistakes The author has just tried to fit things here and there from different sources At many Sticky Church places you can it has used wrong steps just to the fit the answer much like when it happens when you are copying things from otherlaces without understanding it It will just confuse you if you want to understand RL well Looks like some if not all comments are given by Forbidden Love Unchained people associated with author as from no standards it is a good book If you just want to get a hang of it it may be good but that way there are many free better books available to serve thaturpose I really regret buying it reinforcement learning made simple Simple solid math when needed with good Witches of the Deep South python codeSolid introduction to reinforcement learning traditional strategies and modern deep reinforcement learningDefinitively recommend Text is readable but the formu. Uce you to deep reinforcement learning algorithms such as Dueling DN DRN A3C PPO and TRPO You will also learn about imagination augmented agents learning from humanreference DfD HER and many of the recent advancements in reinforcement learningBy the end of the book you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your Metro 2033 (Universo Metro) projects and you will be all set to enter the world of artificial intelligenceWhat 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 Bellmans optimality and TD learningSolve multi armed banditroblems using various algorithmsMaster deep learning algorithms such as RNN LSTM and CNN with applicationsBuild intelligent agents using the DRN algorithm to lay the Doom gameTeach agen.
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Las shows up unreadable on Kindle The book starts with building strong foundations to Reinforcement Learning and then explains deep reinforcement learning algorithms I really liked the way author has explained advanced concepts in such a simple and intuitive way Also I never know I can understand math so simply Building Applications like training robot to walk building car racing agent lunar lander really makes it fun while learning Overall awesome book I have not gone through all the chapters yet but this looks romising for detailed knowledge Great to have this book Clear description and implementation on those RL algorithm But this book cannot tell you why it works No Run for Your Life (Michael Bennett, proof or comparison on different algorithm Somewhat helpful but too complex a topic for a book written by someone for which English may not be a first language Author tries and seems to know their stuf. Ts tolay the Lunar Lander game using DDPGTrain an agent to win a car racing game using dueling DNWho this book is forIf youre 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 Alice-Miranda at Camp programming language will help you understand the concepts covered in this book Table of ContentsIntroduction to Reinforcement LearningGetting started with OpenAI and TensorflowMarkov Decisionrocess and Dynamic ProgrammingGaming with Monte Carlo Tree SearchTemporal Difference LearningMulti Armed Bandit ProblemDeep Learning FundamentalsDeep Learning and ReinforcementPlaying Doom With Deep Recurrent NetworkAsynchronous Advantage Actor Critic NetworkPolicy Gradients and OptimizationCapstone Project Car Racing using DNCurrent Research and Next Steps.
Sudharsan Ravichandiran is a data scientist researcher and best selling author He has completed his bachelors in information technology from Anna University His area of research comprises practical implementations of deep learning and reinforcement learning which includes natural language processing and computer vision He used to be a freelance web developer and designer and has designed award winning websites He is an open source contributor and loves answering uestions on Stack Overflow