El jueves Noviembre 21 2019 

Deep Reinforcement Learning Hands-on: Apply Modern Rl Methods, With Deep Q-networks, Value Iteration, Policy Gradients, Trpo, Alphago Zero And More por Maxim Lapan

Deep Reinforcement Learning Hands-on: Apply Modern Rl Methods, With Deep Q-networks, Value Iteration, Policy Gradients, Trpo, Alphago Zero And More por Maxim Lapan

Descargar Deep Reinforcement Learning Hands-on: Apply Modern Rl Methods, With Deep Q-networks, Value Iteration, Policy Gradients, Trpo, Alphago Zero And More por Maxim Lapan Epub y PDF

  • Título: Deep Reinforcement Learning Hands-on: Apply Modern Rl Methods, With Deep Q-networks, Value Iteration, Policy Gradients, Trpo, Alphago Zero And More
  • Autores: Maxim Lapan
  • Publicado: 21 de junio de 2018
  • ISBN: 1788834240
  • Tamaño: 15 MB
  • Nº de páginas: 546
  • Idiomas: Español
  • Valoración: ★★★★★
  • Formato : PDF, EPUB


Maxim Lapan con Deep Reinforcement Learning Hands-on: Apply Modern Rl Methods, With Deep Q-networks, Value Iteration, Policy Gradients, Trpo, Alphago Zero And More

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.Key FeaturesExplore 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, evolution strategies and genetic algorithmsKeep up with the very latest industry developments, including AI-driven chatbotsBook DescriptionRecent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.What you will learnUnderstand the DL context of RL and implement complex DL modelsLearn the foundation of RL: Markov decision processesEvaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and othersDiscover how to deal with discrete and continuous action spaces in various environmentsDefeat Atari arcade games using the value iteration methodCreate 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 chatbotsWho 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.Table of ContentsWhat is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksDQN ExtensionsStocks Trading Using RLPolicy Gradients – An AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticChatbots Training with RLWeb NavigationContinuous Action SpaceTrust Regions – TRPO, PPO, and ACKTRBlack-Box Optimization in RLBeyond Model-Free – ImaginationAlphaGo Zero
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