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)

Summary

In this chapter, we learned how neural networks actually work followed by building a neural network to classify handwritten digits using TensorFlow. We also saw different types of neural networks such as an RNN, which can remember information in the memory. Then, we saw the LSTM network, which is used to overcome the vanishing gradient problem by keeping several gates to retain information in the memory as long as it is required. We also saw another interesting neural network for recognizing images called CNN. We saw how CNN use different layers to understand the image. Following this, we learned how to build a CNN to recognize fashion products using TensorFlow.

In the next chapter, Chapter 8, Atari Games With Deep Q Network, we will see how neural networks will actually help our RL agents to learn more efficiently.