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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow.
Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Ravichandiran, Sudharsan. | |
| 245 | 1 | 0 | |a Hands-On Reinforcement Learning with Python : |b Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (309 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; 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 | 8 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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. | |
| 505 | 8 | |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: Deep Learning Fundamentals; Artificial neurons; ANNs; Input layer; Hidden layer; Output layer. | |
| 500 | |a Activation functions. | ||
| 520 | |a Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Human-computer interaction. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Social Aspects |x Human-Computer Interaction. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Ravichandiran, Sudharsan. |t Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788836524 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5439844 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0037018846 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5439844 | ||
| 994 | |a 92 |b IZTAP | ||