ID3 as described in a recent post requires a discrete and single evaluation, e.g. do this or don’t do this, good or bad, etc., but the evaluations in the training data will be a wide range of values.
Therefore, we could just normalize the ranging values (from reinforcement training of the creature), and then flip a coin that is biased based on the resulting values to determine if a given action is good or bad. Then we could construct the decision tree. This requires rebuilding the decision tree every time we make a choice, i.e. every time a biological need / user input arises… but the trees should be fairly small so this is okay.