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008190427s2019 enk o 000 0 eng d
019 ▼a 1099338263
020 ▼a 1838640142
020 ▼a 9781838640149 ▼q (electronic bk.)
035 ▼a 2108228 ▼b (N$T)
035 ▼a (OCoLC)1099341025 ▼z (OCoLC)1099338263
040 ▼a EBLCP ▼b eng ▼e pn ▼c EBLCP ▼d UKAHL ▼d OCLCQ ▼d YDX ▼d OCLCQ ▼d N$T ▼d 248032
049 ▼a MAIN
050 4 ▼a QA76.73.P98
08204 ▼a 006.31 ▼2 23
24500 ▼a Python Reinforcement Learning : ▼b Solve Complex Real-World Problems by Mastering Reinforcement Learning Algorithms Using OpenAI Gym and TensorFlow / ▼c Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani and Yang Wenzhuo.
260 ▼a Birmingham : ▼b Packt Publishing, Limited, ▼c 2019.
300 ▼a 1 online resource (484 pages)
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
338 ▼a online resource ▼b cr ▼2 rdacarrier
500 ▼a Implementation of the Atari emulator
5050 ▼a Cover; 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
5058 ▼a OpenAI 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
5058 ▼a ConstantsPlaceholders; 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
5058 ▼a Solving 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
5058 ▼a Solving 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 ▼a 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.
5880 ▼a Print version record.
590 ▼a Master record variable field(s) change: 050, 082, 650
650 0 ▼a Python (Computer program language)
650 0 ▼a Reinforcement learning.
655 4 ▼a Electronic books.
7001 ▼a Ravichandiran, Sudharsan, ▼e author.
7001 ▼a Saito, Sean, ▼e author.
7001 ▼a Shanmugamani, Rajalingappaa, ▼e author.
7001 ▼a Wenzhuo, Yang, ▼e author.
77608 ▼i Print version: ▼a Ravichandiran, Sudharsan. ▼t Python Reinforcement Learning : Solve Complex Real-World Problems by Mastering Reinforcement Learning Algorithms Using OpenAI Gym and TensorFlow. ▼d Birmingham : Packt Publishing, Limited, 짤2019 ▼z 9781838649777
85640 ▼3 EBSCOhost ▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2108228
938 ▼a Askews and Holts Library Services ▼b ASKH ▼n AH36202699
938 ▼a ProQuest Ebook Central ▼b EBLB ▼n EBL5755722
938 ▼a YBP Library Services ▼b YANK ▼n 300481873
938 ▼a EBSCOhost ▼b EBSC ▼n 2108228
990 ▼a 관리자
994 ▼a 92 ▼b N$T