Hands-On Intelligent Agents with OpenAI Gym

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Completing the OpenAI Gym setup

Let's update our version of pip first:

(rl_gym_book) praveen@ubuntu:~$ pip install --ignore-installed pip

Then, let's download the source code of OpenAI Gym from the GitHub repository into our home folder:

(rl_gym_book) praveen@ubuntu:~$cd ~

(rl_gym_book) praveen@ubuntu:~$git clone https://github.com/openai/gym.git

(rl_gym_book) praveen@ubuntu:~$cd gym

If you get an error saying git command not found or something similar, you might have to install Git. On Ubuntu systems, you can install it by running this command, sudo apt-get install git. On macOS, if you don't have Git installed already, it will prompt you to install it when you run the git clone command.

We are now in the final stage of a complete Gym installation! If you got a MuJoCo license and followed the MuJoCo installation instructions successfully, then you can go ahead and complete a full installation by running the following command:

(rl_gym_book) praveen@ubuntu:~/gym$pip install -e '.[all]'

If you did not install MuJoCo, then this command will return errors. We will be installing the Gym environments that we will be using, other than MuJoCo (which requires a license to use). Make sure that you are still in the gym directory under your home folder, and also make sure that you are still inside the rl_gym_book conda environment. Your prompt should include the rl_gym_book prefix as follows, where ~/gym means that the prompt is at the gym directory under the home folder:

(rl_gym_book) praveen@ubuntu:~/gym$

Here is a table summarizing the installation commands for installing the environments that have been discussed in Chapter 1, Introduction to Intelligent Agents and Learning Environments.

    
Environment           
Installation command

      
Atari           
pip install -e '.[atari]'

      
Box2D           

          
pip install -e '.[box2d]'

          
conda install -c https://conda.anaconda.org/kne pybox2d
        

      
Classic control           

          
pip install -e '.[classic_control]'
        

      
MuJoCo (requires license)           
pip install -e '.[mujoco]'

      
Robotics (requires license)           

          
pip install -e '.[robotics]'

Let's go ahead and install the environments we do not need a license to use. Run the following commands to install Atarti, Box2D, and the classic control environments:

(rl_gym_book) praveen@ubuntu:~/gym$pip install -e '.[atari]'

(rl_gym_book) praveen@ubuntu:~/gym$pip install -e '.[box2d]'

(rl_gym_book) praveen@ubuntu:~/gym$conda install -c https://conda.anaconda.org/kne pybox2d

(rl_gym_book) praveen@ubuntu:~/gym$pip install -e '.[classic_control]'

We are all set! We'll quickly run a check to make sure the installation went fine. Copy and paste the following code snippet into a file named test_box2d.py under the ~/rl_gym_book directory:

#!/usr/bin/env python
import gym
env = gym.make('BipedalWalker-v2')
env.reset()
for _ in range(1000):
    env.render()
    env.step(env.action_space.sample())

Run that code using the following commands:

(rl_gym_book) praveen@ubuntu:~/gym$cd ~/rl_gym_book

(rl_gym_book) praveen@ubuntu:~/rl_gym_book$python test_box2d.py

You will see a window pop up that shows the BipedalWalker-v2 environment and the walker trying to randomly perform some actions:

So, we have the Gym environment set up. What's next, you may ask. In the next section, we will set up the tools and libraries we need to develop deep reinforcement learning agents to train in these environments!