{"product_id":"deep-reinforcement-learning-hands-on-apply-modern-rl-methods-with-deep-q-networks-value-iteration-policy-gradients-trpo-alphago-zero-and-more-paperback","title":"Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMaxim Lapan\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePublisher's Note: This edition from 2018 is outdated and not compatible with any of the most recent updates to Python libraries. A new third edition, updated for 2020 with six new chapters that include multi-agent methods, discrete optimization, RL in robotics, and advanced exploration techniques is now available.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThis practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplore deep reinforcement learning (RL), from the first principles to the latest algorithms\u003c\/li\u003e \u003cli\u003eEvaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms\u003c\/li\u003e \u003cli\u003eKeep up with the very latest industry developments, including AI-driven chatbots\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eDeep 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.\u003c\/p\u003e \u003cp\u003eThe 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.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat you will learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUnderstand the DL context of RL and implement complex DL models\u003c\/li\u003e \u003cli\u003eLearn the foundation of RL: Markov decision processes\u003c\/li\u003e \u003cli\u003eEvaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others\u003c\/li\u003e \u003cli\u003eDiscover how to deal with discrete and continuous action spaces in various environments\u003c\/li\u003e \u003cli\u003eDefeat Atari arcade games using the value iteration method\u003c\/li\u003e \u003cli\u003eCreate your own OpenAI Gym environment to train a stock trading agent\u003c\/li\u003e \u003cli\u003eTeach your agent to play Connect4 using AlphaGo Zero\u003c\/li\u003e \u003cli\u003eExplore the very latest deep RL research on topics including AI-driven chatbots\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWho this book is for\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eSome 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.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 546\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.1 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 20, 2018\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52492856099123,"sku":"9781788834247","price":77.54,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/STFXb05LQ2JYVWlyTXFYN0cwT2R1QT09.webp?v=1759942787","url":"https:\/\/www.vysn.com\/products\/deep-reinforcement-learning-hands-on-apply-modern-rl-methods-with-deep-q-networks-value-iteration-policy-gradients-trpo-alphago-zero-and-more-paperback","provider":"VYSN","version":"1.0","type":"link"}