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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , , , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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