CartPole
In this section, we will make use of Open AI Gym, a set of environments containing non-trivial elementary problems that can be solved using RL approaches. We'll use the CartPole environment. The objective of the agent is to learn how to keep a pole balanced on a moving cart, with possible actions including a movement to the left or to the right:
Figure 11.3: The CartPole environment, with the black cart balancing a long pole
Now we understand our environment, let's build a model to balance a pole.
How do we go about it?
We begin by installing some prerequisites and importing the necessary libraries. The installation part is mostly required to ensure that we can generate visualizations of the trained agent's performance:
!sudo apt-get install -y xvfb ffmpeg
!pip install gym
!pip install 'imageio==2.4.0'
!pip install PILLOW
!pip install pyglet
!pip install pyvirtualdisplay
!pip install tf-agents
from __future__ import absolute_import...
