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The Reinforcement Learning Workshop Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems.

With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intellige...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Palmas, Alessandro
Otros Autores: Ghelfi, Emanuele, Petre, Alexandra Galina, Kulkarni, Mayur, N.S., Anand, Nguyen, Quan, Sen, Aritra, So, Anthony (Data scientist), Basak, Saikat
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2020.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Palmas, Alessandro. 
245 1 4 |a The Reinforcement Learning Workshop  |h [electronic resource] :  |b Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems. 
260 |a Birmingham :  |b Packt Publishing, Limited,  |c 2020. 
300 |a 1 online resource (821 p.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a Description based upon print version of record. 
505 0 |a Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Reinforcement Learning -- Introduction -- Learning Paradigms -- Introduction to Learning Paradigms -- Supervised versus Unsupervised versus RL -- Classifying Common Problems into Learning Scenarios -- Predicting Whether an Image Contains a Dog or a Cat -- Detecting and Classifying All Dogs and Cats in an Image -- Playing Chess -- Fundamentals of Reinforcement Learning -- Elements of RL -- Agent -- Actions -- Environment -- Policy -- An Example of an Autonomous Driving Environment 
505 8 |a Exercise 1.01: Implementing a Toy Environment Using Python -- The Agent-Environment Interface -- What's the Agent? What's in the Environment? -- Environment Types -- Finite versus Continuous -- Deterministic versus Stochastic -- Fully Observable versus Partially Observable -- POMDP versus MDP -- Single Agents versus Multiple Agents -- An Action and Its Types -- Policy -- Stochastic Policies -- Policy Parameterizations -- Exercise 1.02: Implementing a Linear Policy -- Goals and Rewards -- Why Discount? -- Reinforcement Learning Frameworks -- OpenAI Gym -- Getting Started with Gym -- CartPole 
505 8 |a Gym Spaces -- Exercise 1.03: Creating a Space for Image Observations -- Rendering an Environment -- Rendering CartPole -- A Reinforcement Learning Loop with Gym -- Exercise 1.04: Implementing the Reinforcement Learning Loop with Gym -- Activity 1.01: Measuring the Performance of a Random Agent -- OpenAI Baselines -- Getting Started with Baselines -- DQN on CartPole -- Applications of Reinforcement Learning -- Games -- Go -- Dota 2 -- StarCraft -- Robot Control -- Autonomous Driving -- Summary -- Chapter 2: Markov Decision Processes and Bellman Equations -- Introduction -- Markov Processes 
505 8 |a The Markov Property -- Markov Chains -- Markov Reward Processes -- Value Functions and Bellman Equations for MRPs -- Solving Linear Systems of an Equation Using SciPy -- Exercise 2.01: Finding the Value Function in an MRP -- Markov Decision Processes -- The State-Value Function and the Action-Value Function -- Bellman Optimality Equation -- Solving the Bellman Optimality Equation -- Solving MDPs -- Algorithm Categorization -- Value-Based Algorithms -- Policy Search Algorithms -- Linear Programming -- Exercise 2.02: Determining the Best Policy for an MDP Using Linear Programming -- Gridworld 
505 8 |a Activity 2.01: Solving Gridworld -- Summary -- Chapter 3: Deep Learning in Practice with TensorFlow 2 -- Introduction -- An Introduction to TensorFlow and Keras -- TensorFlow -- Keras -- Exercise 3.01: Building a Sequential Model with the Keras High-Level API -- How to Implement a Neural Network Using TensorFlow -- Model Creation -- Model Training -- Loss Function Definition -- Optimizer Choice -- Learning Rate Scheduling -- Feature Normalization -- Model Validation -- Performance Metrics -- Model Improvement -- Overfitting -- Regularization -- Early Stopping -- Dropout -- Data Augmentation 
500 |a Batch Normalization. 
520 |a With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Reinforcement learning. 
650 0 |a Algorithms. 
650 2 |a Algorithms 
650 6 |a Apprentissage par renforcement (Intelligence artificielle) 
650 6 |a Algorithmes. 
650 7 |a algorithms.  |2 aat 
650 7 |a Programming & scripting languages: general.  |2 bicssc 
650 7 |a Artificial intelligence.  |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 Programming Languages  |x Python.  |2 bisacsh 
650 7 |a Algorithms  |2 fast 
650 7 |a Reinforcement learning  |2 fast 
700 1 |a Ghelfi, Emanuele. 
700 1 |a Petre, Alexandra Galina. 
700 1 |a Kulkarni, Mayur. 
700 1 |a N.S., Anand. 
700 1 |a Nguyen, Quan. 
700 1 |a Sen, Aritra. 
700 1 |a So, Anthony  |c (Data scientist) 
700 1 |a Basak, Saikat. 
776 0 8 |i Print version:  |a Palmas, Alessandro  |t The Reinforcement Learning Workshop : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems  |d Birmingham : Packt Publishing, Limited,c2020  |z 9781800200456 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781800200456/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
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