Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
24
Other Books You May Enjoy
25
Index

Creating an environment

We will be using a package called OpenAI Gym to build RL agents. You can learn more about it here: https://gym.openai.com. It can be installed using pip by running the following command:

$ pip3 install gym

You can find various tips and tricks related to its installation here:

https://github.com/openai/gym#installation

Now that you have installed it, let's go ahead and write some code.

Create a new Python file and import the following packages:

import argparse
import gym

Define a function to parse the input arguments. The input arguments will be used to specify the type of environment to be run:

def build_arg_parser():
    parser = argparse.ArgumentParser(description='Run an environment')
    parser.add_argument('--input-env', dest='input_env', required=True,
            choices=['cartpole', 'mountaincar', 'pendulum', 'taxi', 'lake'], 
        ...