I liked this book very "Muc. Apply Modern Reinforcement " Apply modern
Reinforcement And Deep Reinforcement and deep reinforcement methods sing Python and its powerful librariesKey FeaturesYour entry point into the world of artificial intelligence sing the power of PythonAn example rich guide to master various RL and DRL algorithmsExplore the power of modern Python libraries to gain confidence in building self trained applicationsBook DescriptionReinforcement Learning RL is the trending and most promising branch of artificial intelligence This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithmsThe Learning Path starts with an introduction to RL followed by OpenAI Gym and TensorFlow You will then explore various RL algorithms such as Markov Decision Process Monte Carlo methods and dynamic programming including value and policy iteration Youll also work on various datasets including image text and video This example rich guide will introduce you to deep RL algorithms such as Dueling DN DRN A3C. ,
Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo õ 5 Free read,
H Very practical and hands. PPO and TRPO You will gain "Experience In Several Domains "in several domains gaming image processing and physical simulations Youll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices generate natural language and even build other neural networks You will also learn about imagination augmented agents learning from human preference DfD HER and many of the recent advancements in RLBy the end of the Learning Path you will have all the
Knowledge And Experience Needed Toand experience needed to RL and deep RL in your projects and you enter the world of artificial intelligence to solve various real life problemsThis Learning Path includes content from the following
Packt ProductsHands On Reinforcement LearningproductsHands On Reinforcement Learning Python by Sudharsan RavichandiranPython Reinforcement Learning Projects by Sean Saito Yang Wenzhuo and Rajalingappaa ShanmugamaniWhat you will learnTrain an agent to walk sing OpenAI Gym and TensorFlowSolve multi armed bandit problems The Greek Tycoons Mistress using various algorithmsBuild intelligent agentssing the DRN algorithm to. ,
On RL is exciting as it get. Play the Doom gameTeach your agent to play Connect4 Bidding on Her Boss (The Hawke Brothers, using AlphaGo ZeroDefeat Atari arcade gamessing the value iteration methodDiscover how to deal with discrete and continuous action spaces in various environmentsWho this book is forIf youre an MLDL enthusiast interested in AI and want to explore RL and deep RL from scratch this Learning Path is for you Prior knowledge of linear algebra is expected Table of ContentsIntroduction to Reinforcement LearningGetting Started with OpenAI and TensorFlowThe Markov Decision Process and Dynamic ProgrammingGaming with Monte Carlo MethodsTemporal Difference LearningMulti Armed Bandit ProblemPlaying Atari GamesAtari Games with Deep NetworkPlaying Doom with a Deep Recurrent NetworkThe Asynchronous Advantage Actor Critic NetworkPolicy Gradients and OptimizationBalancing CartPoleSimulating
Control TasksBuilding Virtual Worlds in MinecraftLearning to Play GoCreating a ChatbotGenerating a DeepTasksBuilding Virtual Worlds in MinecraftLearning to Play GoCreating a ChatbotGenerating a Deep Image ClassifierPredicting "Future Stock PricesCapstone Project Car Racing Using "Stock PricesCapstone Project Car Racing Using Ahe.