Why do DQNs tend to forget? Why do DQNs tend to forget? Is it because when you feed highly correlated samples, your model (function approximation) doesn't give a general solution?
For example:

I use level 1 experiences, my model $p$ is fitted to learn how to play that level.

I go to level 2, my weights are updated and fitted to play level 2 meaning I don't know how to play level 1 again.
You are referring to catastrophic forgetting which could be an issue in any neural net. More specifically for DQN refer to this article.