Baseline
In this chapter, we will take the Atari Pong environment that you are already familiar with and try to speed up its convergence. As a baseline, we will take the same simple DQN that we used in Chapter 8, and the hyperparameters will also be the same. To compare the effect of our changes, we will use two characteristics:
-
The number of frames that we consume from the environment every second (FPS). This indicates how fast we can communicate with the environment during the training. It is very common in RL papers to indicate the number of frames that the agent observed during the training; normal numbers are 25M–50M frames. So, if our FPS=200, it will take
≈ 2.89 days. In such calculations, you need to take into account that RL papers commonly report raw environment frames. But if frame skip is used (and it almost always is), the count of frames needs to be divided by this factor, which is commonly equal to 4. In our measurements...
