Book Image

Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Reinforcement Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)

Convolutional neural networks

CNN, also known as ConvNet, is a special type of neural network and it is extensively used in Computer Vision. The application of a CNN ranges from enabling vision in self-driving cars to the automatic tagging of friends in your Facebook pictures. CNNs make use of spatial information to recognize the image. But how do they really work? How can the neural networks recognize these images? Let's go through this step by step.

A CNN typically consists of three major layers:

  • Convolutional layer
  • Pooling layer
  • Fully connected layer

Convolutional layer

When we feed an image as input, it will actually be converted to a matrix of pixel values. These pixel values range from 0 to 255 and the dimensions...