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000		camMi
001		2210080896889
003		OCoLC
005		20210225114906
006		m d
007		cr \|n\|---\|\|\|\|\|
008		190427s2019 enk o 000 0 eng d
019		▼a1099338263
020		▼a1838640142
020		▼a9781838640149▼q(electronic bk.)
035		▼a2108228▼b(N▼T)
035		▼a(OCoLC)1099341025▼z(OCoLC)1099338263
040		▼aEBLCP▼beng▼epn▼cEBLCP▼dUKAHL▼dOCLCQ▼dYDX▼dOCLCQ▼dN▼d221008
050		▼aQA76.73.P98
082		▼a006.31▼223
245	00	▼aPython Reinforcement Learning :▼bSolve Complex Real-World Problems by Mastering Reinforcement Learning Algorithms Using OpenAI Gym and TensorFlow /▼cSudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani and Yang Wenzhuo.
260		▼aBirmingham :▼bPackt Publishing, Limited,▼c2019.
300		▼a1 online resource (484 pages)
336		▼atext▼btxt▼2rdacontent
337		▼acomputer▼bc▼2rdamedia
338		▼aonline resource▼bcr▼2rdacarrier
500	00	▼aImplementation of the Atari emulator
505		▼aCover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Reinforcement Learning; What is RL?; RL algorithm; How RL differs from other ML paradigms; Elements of RL; Agent; Policy function; Value function; Model; Agent environment interface; Types of RL environment; Deterministic environment; Stochastic environment; Fully observable environment; Partially observable environment; Discrete environment; Continuous environment; Episodic and non-episodic environment; Single and multi-agent environment; RL platforms
505		▼aOpenAI Gym and UniverseDeepMind Lab; RL-Glue; Project Malmo; ViZDoom; Applications of RL; Education; Medicine and healthcare; Manufacturing; Inventory management; Finance; Natural Language Processing and Computer Vision; Summary; Questions; Further reading; Chapter 2: Getting Started with OpenAI and TensorFlow; Setting up your machine; Installing Anaconda; Installing Docker; Installing OpenAI Gym and Universe; Common error fixes; OpenAI Gym; Basic simulations; Training a robot to walk; OpenAI Universe; Building a video game bot; TensorFlow; Variables, constants, and placeholders; Variables
505		▼aConstantsPlaceholders; Computation graph; Sessions; TensorBoard; Adding scope; Summary; Questions; Further reading; Chapter 3: The Markov Decision Process and Dynamic Programming; The Markov chain and Markov process; Markov Decision Process; Rewards and returns; Episodic and continuous tasks; Discount factor; The policy function; State value function; State-action value function (Q function); The Bellman equation and optimality; Deriving the Bellman equation for value and Q functions; Solving the Bellman equation; Dynamic programming; Value iteration; Policy iteration
505		▼aSolving the frozen lake problemValue iteration; Policy iteration; Summary; Questions; Further reading; Chapter 4: Gaming with Monte Carlo Methods; Monte Carlo methods; Estimating the value of pi using Monte Carlo; Monte Carlo prediction; First visit Monte Carlo; Every visit Monte Carlo; Let's play Blackjack with Monte Carlo; Monte Carlo control; Monte Carlo exploration starts; On-policy Monte Carlo control; Off-policy Monte Carlo control; Summary; Questions; Further reading; Chapter 5: Temporal Difference Learning; TD learning; TD prediction; TD control; Q learning
505		▼aSolving the taxi problem using Q learningSARSA; Solving the taxi problem using SARSA; The difference between Q learning and SARSA; Summary; Questions; Further reading; Chapter 6: Multi-Armed Bandit Problem; The MAB problem; The epsilon-greedy policy; The softmax exploration algorithm; The upper confidence bound algorithm; The Thompson sampling algorithm; Applications of MAB; Identifying the right advertisement banner using MAB; Contextual bandits; Summary; Questions; Further reading; Chapter 7: Playing Atari Games; Introduction to Atari games; Building an Atari emulator; Getting started
520		▼aReinforcement learning and deep reinforcement learning are the trending and most promising branches of artificial intelligence. This Learning Path will enable you to master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms and their limitations.
588		▼aPrint version record.
590		▼aMaster record variable field(s) change: 050, 082, 650
650		▼aPython (Computer program language)
650		▼aReinforcement learning.
655		▼aElectronic books.
700		▼aRavichandiran, Sudharsan,▼eauthor.
700		▼aSaito, Sean,▼eauthor.
700		▼aShanmugamani, Rajalingappaa,▼eauthor.
700		▼aWenzhuo, Yang,▼eauthor.
776		▼iPrint version:▼aRavichandiran, Sudharsan.▼tPython Reinforcement Learning : Solve Complex Real-World Problems by Mastering Reinforcement Learning Algorithms Using OpenAI Gym and TensorFlow.▼dBirmingham : Packt Publishing, Limited, 짤2019▼z9781838649777
856		▼3EBSCOhost▼uhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2108228
938		▼aAskews and Holts Library Services▼bASKH▼nAH36202699
938		▼aProQuest Ebook Central▼bEBLB▼nEBL5755722
938		▼aYBP Library Services▼bYANK▼n300481873
938		▼aEBSCOhost▼bEBSC▼n2108228
994		▼a92▼bN

Python Reinforcement Learning :Solve Complex Real-World Problems by Mastering Reinforcement Learning Algorithms Using OpenAI Gym and TensorFlow /Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani and Yang Wenzhuo

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저자명

Ravichandiran Sudharsan author Saito Sean author Shanmugamani Rajalingappaa author Wenzhuo Yang author

발행사항

Birmingham : Packt Publishing, Limited 2019.

형태사항

1 online resource (484 pages)

주기사항

Implementation of the Atari emulator / Reinforcement learning and deep reinforcement learning are the trending and most promising branches of artificial intelligence. This Learning Path will enable you to master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms and their limitations.

ISBN

1838640142 9781838640149

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