Building a DQN model in PyTorch

We discussed the theory behind DQNs in the previous section. In this section, we will take a hands-on approach. Using PyTorch, we will build a CNN-based DQN model that will train an agent to play the video game known as Pong. The goal of this exercise is to demonstrate how to develop DRL applications using PyTorch. Let's get straight into the exercise.

Initializing the main and target CNN models

In this exercise, we will only show the important parts of the code for demonstration purposes. In order to access the full code, visit https://github.com/PacktPublishing/Mastering-PyTorch/blob/master/Chapter09/pong.ipynb. Follow these steps:

1. First, we need to import the necessary libraries:

   # general imports
   import cv2
   import math
   import numpy as np
   import random
   # reinforcement learning related imports
   import re
   import atari_py as ap
   from collections import deque
   from gym import make, ObservationWrapper, Wrapper
   from gym.spaces import Box...