[Télécharger] Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimisation, web automation and more (English Edition) de Maxim Lapan Livres En Ligne
Télécharger Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimisation, web automation and more (English Edition) de Maxim Lapan En Ligne

Télécharger "Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimisation, web automation and more (English Edition)" de Maxim Lapan Francais PDF
Auteur : Maxim Lapan
Catégorie : Boutique Kindle,Ebooks Kindle,Ebooks en langues étrangères
Broché : * pages
Éditeur : *
Langue : Français, Anglais
A new edition of the bestselling guide to Deep Reinforcement Learning and how it can be used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and moreKey Features2nd edition of the bestselling introduction to deep reinforcement learning, expanded with six new chaptersExplore deep reinforcement learning (RL), from the first principles to the latest algorithmsEvaluate high-profile RL methods, including Value Iteration, Deep Q-networks, Policy Gradients, TRPO, PPO, DDPG, D4PG and non-gradient methodsDiscover ways to increase efficiency of RL methods both from theoretical and engineering perspectiveLearn advanced exploration techniques including Noisy Networks, Pseudo-count and Network Distillation methodsApply RL methods to cheap hardware robotics platformBook DescriptionDeep Reinforcement Learning Hands-On 2nd edition is an updated and expanded version of the bestselling guide to the very latest RL tools and techniques. It provides you with an introduction to the fundamentals of Reinforcement Learning (RL) while providing you with the practical hands-on ability to code intelligent learning agents to perform a range of practical tasks.With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik’s Cube), multi-agent methods, Microsoft’s TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.In addition, you will gain actionable insights into such topic areas as Deep Q-Networks, Policy Gradient methods, Continuous control problems and highly scalable non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve Pong environment in just 30 minutes of training using step-by-step code optimization.In short, Deep Reinforcement Learning 2nd edition is your companion to navigating the exciting complexities of RL as it helps you attain expert hands-on experience and knowledge through practical real-world examples.What you will learnUnderstand the DL context of RL and implement complex DL modelsLearn the foundation of RL: Markov chain, Markov reward processes and Markov decision processesEvaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and othersFind cutting edge ways to speed up RL methodsBuild a practical hardware robot trained with RL methods for less than $100Discover Microsoft’s TextWorld Environment which is an Interactive Fiction games platformUse discrete optimization in RL to solve the Rubik’s CubeGain insights into dealing with discrete and continuous action spaces in various environmentsCreate your own OpenAI Gym environment to train a stock trading agentTeach your agent to play Connect4 using AlphaGo ZeroExplore the very latest deep RL research on topics including AI-driven chatbotsDiscover advanced exploration techniques, including Noisy Networks and Network Distillation techniques Who This Book Is ForSome fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
Télécharger Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimisation, web automation and more (English Edition) de Maxim Lapan Livres Pdf Epub
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