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|z (OCoLC)1193132561
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|a 9781800209961
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|b .P35 2020
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|a 006.31
|2 23
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049 |
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|a UAMI
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100 |
1 |
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|a Palmas, Alessandro.
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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 |
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|a Birmingham :
|b Packt Publishing, Limited,
|c 2020.
|
300 |
|
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|a 1 online resource (821 p.)
|
336 |
|
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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500 |
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|a Description based upon print version of record.
|
505 |
0 |
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|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 |
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|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 |
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|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
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505 |
8 |
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|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
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505 |
8 |
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|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 |
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|a Batch Normalization.
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520 |
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|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 |
|
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|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 |
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|a Petre, Alexandra Galina.
|
700 |
1 |
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|a Kulkarni, Mayur.
|
700 |
1 |
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|a N.S., Anand.
|
700 |
1 |
|
|a Nguyen, Quan.
|
700 |
1 |
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|a Sen, Aritra.
|
700 |
1 |
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|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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938 |
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|a ProQuest Ebook Central
|b EBLB
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|a YBP Library Services
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