In this section, we will implement NAS. In particular, our Controller is tasked with generating child network architectures that learn to classify images from the CIFAR-10
dataset. The architecture of the child network will be represented by a list of numbers. Every four values in this list represent a convolutional layer in the child network, each describing the kernel size, stride length, number of filters, and the pooling window size in the subsequent pooling layer. Moreover, we specify the number of layers in a child network as a hyper-parameters. For example, if our child network has three layers, its architecture is represented as a vector of length 12. If we have an architecture represented as [3, 1, 12, 2, 5, 1, 24, 2]
, then the child network is a two-layer network where the first layer has kernel size of 3, stride length of 1, 12 filters, and a max-pooling window size of 2, and the second layer has kernel size of 5, stride length of 1, 24 filters, and max-pooling...
Python Reinforcement Learning
By :
Python Reinforcement Learning
By:
Overview of this book
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.
The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.
By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.
This Learning Path includes content from the following Packt products:
• Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran
• Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani
Table of Contents (27 chapters)
Title Page
About Packt
Contributors
Preface
Free Chapter
Introduction to Reinforcement Learning
Getting Started with OpenAI and TensorFlow
The Markov Decision Process and Dynamic Programming
Gaming with Monte Carlo Methods
Temporal Difference Learning
Multi-Armed Bandit Problem
Playing Atari Games
Atari Games with Deep Q Network
Playing Doom with a Deep Recurrent Q Network
The Asynchronous Advantage Actor Critic Network
Policy Gradients and Optimization
Balancing CartPole
Simulating Control Tasks
Building Virtual Worlds in Minecraft
Learning to Play Go
Creating a Chatbot
Generating a Deep Learning Image Classifier
Predicting Future Stock Prices
Capstone Project - Car Racing Using DQN
Looking Ahead
Assessments
Other Books You May Enjoy
Index
Customer Reviews