# Why do some DQN implementations not require random exploration but instead emulate all actions?

I've found online some DQN algorithms that (in a problem with a continuous state space and few actions, let's say 2 or 3), at each time step, compute and store (in the memory used for updating) all the possible actions (so all the possible rewards). For example, on page 5 of the paper Deep Q-trading, they say

This means that we don't need a random exploration to sample an action as in many reinforcement learning tasks; instead we can emulate all the three actions to update the Q-network.

How can this be compatible with the exploration-exploitation dilemma, which states that you have to balance the time steps of exploring with the ones of exploiting?