Why we use Categorical (or any other distribution) is a direct result of the policy formulation, where action is defined to be sampled from a distribution, a ~ π(s), with probability of action = π(a|s). It’s an implementation of the algorithm. You may change the distribution (e.g. to Gaussian for continuous actions), but you may not change it to an argmax because that is fundamentally a different algorithm/sampling mechanism (argmax is not a distribution).
Policy-based methods in RL like ActorCritic are different from Q-learning that uses a greedy-selection of action based on it’s Q-estimation (hence the argmax in Q-learning).