# When should I use normal Q learning over a DQN?

From this article here, it says that using a tabular Q function is less scalable than a deep Q network. I assume that this means that the Q table approach works for some environments, but once they become more complex, they aren't as efficient.

For example, the Frozen Lake environment used in the article states that the deep Q network is slower than the Q table. The Frozen Lake environment has a relatively simple environment with 16 states and 4 actions per state. However, in an environment such as a game of snake, there are many more states, making the Q table larger. How should I decide between a Q table and a Deep Q Network?