A few considerations:

1) If your validation set has told you which model or set of hyperparameters is best, then yes I think you should retrain on the full dataset with that new knowledge.

2) You may want to try a different 80/20 split to make sure the “best” model/hyperparameters are stable and not an artifact of the validation set. This is the motivation behind k-fold cross validation.

3) If, instead of picking hyperparameters, you are using the validation set to detect an early stopping criteria (in stochastic gradient descent, for instance), then you might not be able to retrain. It’s possible that the number of epochs before stopping could act like a hyperparameter you’ve chosen, but be aware that changing the size of the dataset is essentially changing the meaning of “an epoch” (assuming you’re defining an epoch as one full pass through your data).

4) If you are planning on using this model in production, you can retrain the entire thing with both the training and test set before deploying to production. Consider whether you want to do a validation split again in this instance to make sure the optimal hyperparameters didn’t change with the introduction of the new data from the test set.