To my knowledge from reading about model-based and model-free reinforcement learning,
DQN and Double DQN are model-free reinforcement learning methods. (Why am I mentioning this, see below):
You should see under the heading "Definition" (you may need to scroll down in the web-page) in the web-page (can be accessed by the above link), it states that: "model-free techniques require extensive experience."
Now extensive experience, depending of how fast you can go through states, can take a few days even weeks...model-free methods require a lot of samples to learn.
Also there are a lot of states: there are (256^128) states. That is a really big number (I'm just emphasizing that training may take a long while)
Due implementation specifics not being supplied in question, I am assuming your implementation is correct, although...you are using RAM. DQN used image data, I very quickly skimmed the Double DQN paper: https://arxiv.org/pdf/1509.06461.pdf . I am assuming they used image data as well because Double DQN was compared to DQN.