| Description |
1 online resource (821 p.) |
| Note |
Description based upon print version of record. |
| Contents |
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 |
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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 |
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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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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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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 |
| Note |
Batch Normalization. |
| Summary |
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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Reinforcement learning.
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Algorithms.
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algorithms. |
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Programming & scripting languages: general. |
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Artificial intelligence. |
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Neural networks & fuzzy systems. |
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Computers -- Intelligence (AI) & Semantics. |
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Computers -- Neural Networks. |
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Computers -- Programming Languages -- Python. |
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Algorithms |
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Reinforcement learning |
| Added Author |
Ghelfi, Emanuele.
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Petre, Alexandra Galina.
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Kulkarni, Mayur.
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N.S., Anand.
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Nguyen, Quan.
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Sen, Aritra.
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So, Anthony (Data scientist) https://id.oclc.org/worldcat/entity/E39PCjGVCDWxcCx8xrFc47wmr3
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Basak, Saikat.
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| Other Form: |
Print version: Palmas, Alessandro The Reinforcement Learning Workshop : Learn How to Apply Cutting-Edge Reinforcement Learning Algorithms to a Wide Range of Control Problems Birmingham : Packt Publishing, Limited,c2020 9781800200456 |
| ISBN |
9781800209961 |
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1800209967 |
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1800200455 |
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9781800200456 |
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