Q-learning uses a table to store all state-action pairs. Q-learning is a model-free RL algorithm, so how could there be the one called Deep Q-learning, as deepdeep means using DNN; or maybe the state-action table (Q-table) is still there but the DNN is only for input reception (ege.g. turning images into vectors)?
Deep Q-network seems to be only the DNN part of the Deep Q-learning programmeprogram, and Q-network seems the short for Deep Q-network.
Q-learning, Deep Q-learning, and Deep Q-network, what are the differences? May be there a comparison table between these 3 terms?
