Equipped with all the technical knowledge about Q-learning, deep neural networks, and DQN, we can finally put it to work and start to warm up the GPU. In this section, we will apply DQN to an Atari environment, Pong. We have chosen Pong rather than all the other Atari environments because it's simpler to solve and thus requires less time, computational power, and memory. That being said, if you have a decent GPU available, you can apply the same exact configuration to almost all the other Atari games (some may require a little bit of fine-tuning). For the same reason, we adopted a lighter configuration compared to the original DQN paper, both in terms of the capacity of the function approximator (that is, fewer weights) and hyperparameters such as a smaller buffer size. This does not compromise the results rather on Pong but might degrade the performance of other games.

First, we will briefly introduce the Atari environment and the preprocessing pipeline before moving on to the DQN implementation.