Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan By Sudharsan Ravichandiran

CHARACTERS Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan

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 artificial 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 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 Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan

The book is poorly written and difficult to follow. The instructions in chapter 2 are messy and horrible.Download a sample before you buy. Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan 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. Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan Quite a poor book. Lots of conceptual mistakes. The author has just tried to fit things here and there from different sources. At many 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 other places 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 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 that purpose). I really regret buying it. Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan 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. Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan 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 book on DQN. Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlowRavichandiran, Sudharsan