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You're reading from  Hands-On Reinforcement Learning with Python

Product typeBook
Published inJun 2018
Reading LevelIntermediate
PublisherPackt
ISBN-139781788836524
Edition1st Edition
Languages
Python
Tools
TensorFlowKeras
Concepts
Reinforcement Learning
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Author (1)
Sudharsan Ravichandiran
Sudharsan Ravichandiran
author image
Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran

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Getting Started with OpenAI and TensorFlow

OpenAI is a non-profit, open source artificial intelligence (AI) research company founded by Elon Musk and Sam Altman that aims to build a general AI. They are sponsored by top industry leaders and top-notch companies. OpenAI comes in two flavors, Gym and Universe, using which we can simulate realistic environments, build reinforcement learning (RL) algorithms, and test our agents in those environments. TensorFlow is an open source machine learning library by Google that is extensively used for numerical computation. We will use OpenAI and TensorFlow for building and evaluating powerful RL algorithms in the upcoming chapters.

In this chapter, you will learn about the following:

  • Setting up your machine by installing Anaconda, Docker, OpenAI Gym, and Universe and TensorFlow
  • Simulating an environment using OpenAI Gym and Universe
  • Training...

Setting up your machine

Installing OpenAI is not a straightforward task; there are a set of steps that have to be correctly followed for setting the system up and running it. Now, let's see how to set up our machine and install OpenAI Gym and Universe.

Installing Anaconda

All the examples in the book use the Anaconda version of Python. Anaconda is an open source distribution of Python. It is widely used for scientific computing and processing a large volume of data. It provides an excellent package management environment. It provides support for Windows, macOS, and Linux. Anaconda comes with Python installed along with popular packages used for scientific computing such as NumPy, SciPy, and so on.

To download Anaconda...

OpenAI Gym

With OpenAI Gym, we can simulate a variety of environments and develop, evaluate, and compare RL algorithms. Let's now understand how to use Gym.

Basic simulations

Let's see how to simulate a basic cart pole environment:

  1. First, let's import the library:
import gym
  1. The next step is to create a simulation instance using the make function:
env = gym.make('CartPole-v0')
  1. Then we should initialize the environment using the reset method:
env.reset()
  1. Then we can loop for some time steps and render the environment at each step:
for _ in range(1000):
    env.render()
    env.step(env.action_space.sample())

The complete code is as follows:

import gym 
env = gym.make('CartPole-v0')
env...

OpenAI Universe

OpenAI Universe provides a wide range of realistic gaming environments. It is an extension to OpenAI Gym. It provides the ability to train and evaluate agents on a wide range of simple to real-time complex environments. It has unlimited access to many gaming environments.

Building a video game bot

Let's learn how to build a video game bot which plays a car racing game. Our objective is that the car has to move forward without getting stuck on any obstacles or hitting other cars.

First, we import the necessary libraries:

import gym
import universe # register universe environment
import random

Then we simulate our car racing environment using the make function:

env = gym.make('flashgames.NeonRace-v0...

TensorFlow

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...

Summary

In this chapter, we learned how to set up our machine by installing Anaconda, Docker, OpenAI Gym, Universe, and TensorFlow. We also learned how to create simulations using OpenAI and how to train agents to learn in an OpenAI environment. Then we came across the fundamentals of TensorFlow followed by visualizing graphs in TensorBoard.

In the next chapter, Chapter 3, The Markov Decision Process and Dynamic Programming we will learn about Markov Decision Process and dynamic programming and how to solve frozen lake problem using value and policy iteration.

Questions

The question list is as follows:

  1. Why and how do we create a new environment in Anaconda?
  2. What is the need for using Docker?
  3. How do we simulate an environment in OpenAI Gym?
  4. How do we check all available environments in OpenAI Gym?
  5. Are OpenAI Gym and Universe the same? If not, what is the reason?
  6. How are TensorFlow variables and placeholders different from each other?
  7. What is a computational graph?
  8. Why do we need sessions in TensorFlow?
  9. What is the purpose of TensorBoard and how do we start it?

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Author (1)

author image
Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran

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