{"product_id":"deep-reinforcement-learning-handson-apply-modern-rl-methods-to-practical-problems-of-chatbots-robotics-discrete-optimization-web-automation-and-more-9781838826994","title":"Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots  robotics  discrete optimization  web automation  and more","description":"\u003cp\u003eRevised and expanded to include multi-agent methods  discrete optimization  RL in robotics  advanced exploration techniques  and more Key Features Second edition of the bestselling introduction to deep reinforcement learning  expanded with six new chapters Learn advanced exploration techniques including noisy networks  pseudo-count  and network distillation methods Apply RL methods to cheap hardware robotics platforms Book Description Deep Reinforcement Learning Hands-On  Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL  along with the 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 Rubiks Cube)  multi-agent methods  Microsofts 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 the Pong environment in just 30 minutes of training using step-by-step code optimization. In short  Deep Reinforcement Learning Hands-On  Second Edition  is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples. What you will learn Understand the deep learning context of RL and implement complex deep learning models Evaluate RL methods including cross-entropy  DQN  actor-critic  TRPO  PPO  DDPG  D4PG  and others Build a practical hardware robot trained with RL methods for less than $100 Discover Microsoft s TextWorld environment  which is an interactive fiction games platform Use discrete optimization in RL to solve a Rubik s Cube Teach your agent to play Connect 4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI chatbots Discover advanced exploration techniques  including noisy networks and network distillation techniques Who this book is for Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL Table of Contents What Is Reinforcement Learning? OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks Higher-Level RL libraries DQN Extensions Ways to Speed up RL Stocks Trading Using RL Policy Gradients The Actor-Critic Method Asynchronous Advantage Actor-Critic Training Chatbots with RL The TextWorld environment Web Navigation Continuous Action Space RL in Robotics Trust Regions Black-Box Optimization in RL Advanced exploration Beyond Model-Free AlphaGo Zero RL in Discrete Optimisation Multi-agent RL\u003c\/p\u003e","brand":"My Store","offers":[{"title":"Default Title","offer_id":45651810844725,"sku":"ByrdShop_1838826998","price":43.43,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0627\/8139\/0901\/files\/9781838826994.jpg?v=1781840899","url":"https:\/\/atxbooks.com\/products\/deep-reinforcement-learning-handson-apply-modern-rl-methods-to-practical-problems-of-chatbots-robotics-discrete-optimization-web-automation-and-more-9781838826994","provider":"ATX Books","version":"1.0","type":"link"}