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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.
| Autor principal: | |
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| Formato: | Printed Book |
| Publicado: |
Birmingham-Mumbai:
Packt,
2018.
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| Edición: | 1 Ed. |
| Materias: |
| LEADER | 03911nam a22001817a 4500 | ||
|---|---|---|---|
| 020 | |a 9781788836524 | ||
| 082 | |a 006.31 |b RAV-H | ||
| 100 | |a Ravichandiran, Sudharsan | ||
| 245 | |a Hands-on reinforcement learning with Python : |b Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow |c Sudharsan Ravichandiran | ||
| 250 | |a 1 Ed. | ||
| 260 | |a Birmingham-Mumbai: |b Packt, |c 2018. | ||
| 300 | |a i-vi+305p. | ||
| 505 | |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. 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. 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. 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. 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 la | ||
| 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. | ||
| 650 | |a Machine learning. Artificial intelligence. Human-computer interaction. | ||
| 942 | |c BK | ||
| 999 | |c 313803 |d 313803 | ||
| 952 | |0 0 |1 0 |4 0 |6 006_310000000000000_RAVH |7 0 |9 335546 |a DCB |b DCB |d 2020-11-24 |l 1 |o 006.31 RAV-H |p DCB3871 |q 2020-12-08 |r 2020-11-24 |s 2020-11-24 |w 2020-11-24 |y BK | ||