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)


TensorFlow is an open source software library from Google which is extensively used for numerical computation. It is widely used for building deep learning models and is a subset of machine learning. It uses data flow graphs that can be shared and executed on many different platforms. Tensor is nothing but a multi-dimensional array, so when we say TensorFlow, it is literally a flow of multi-dimensional arrays (tensors) in the computation graph.

With Anaconda installed, installing TensorFlow becomes very simple. Irrespective of the platform you are using, you can easily install TensorFlow by typing the following command:

source activate universe
conda install -c conda-forge tensorflow
Don't forget to activate the universe environment before installing TensorFlow.

We can check whether the TensorFlow installation was successful by simply running the following Hello...