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-INPUT and OUTPUT OF ATARI DQN:

In the abstract paragraph of the DQN work by DeepMind https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf it has written:

" We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. "

Would you please explain the bold sections?

Input and output refer to Q-learning or DNN?

• Why it is said that learn control policy, however DQN is composed of QL which is a value iteration and"off-policy" algorithm and it is not a policy iteration and "on-policy"?

• What is the meaning of variant type of Q-learning is vague? it is variant because approximation of Q-learning is implemented?

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