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I just watched the reinforcement learning video and was very confused -- where did the reinforcement learning happen? I thought reinforcement learning was giving an agent positive / negative signals to learn without example output data, but in the video the network was just retrained with more data? Is it an overloaded term maybe?

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Ty for asking this question! "Reinforcement learning" has a wide definition, but in this case because we are using a simple feed forward neural network, "reinforce" is more principled by dynamic programming with supervised learning. We are not actually using a "Deep reinforcement learning" algorithm.

The idea here is that the net can continue to train, reinforcing its previous understanding with new understandings, if new training data is provided.

It may be that we need to clarify the tutorial, as your point is based around unsupervised learning, not having training data.

Is it an overloaded term? YES!

Thanks for the clarification!

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