Pavlov.js – Reinforcement learning using Markov Decision Processes

no the reward is temporally separated from the input. It's thought the brains mechanism to do this is somewhat reenforcement learning like (Basal ganglia)

I can definitely understand wanting to classify Pavlov as supervised learning. I think it's a murky issue because supervised and reinforcement are very closely related (and it is frequently possible to reframe problems of one type as problems of the other depending on what type of model one uses).

My two main reasons for going with this name are as follows (if people see issues in my logic, I'm happy to be convinced):

1) Reinforcement learning gets its name from the behaviorist psychology concept of reinforcement in which an agent's actions are met with rewards in order to shape that agent's future behavior. This is precisely the kind of response conditioning that Pavlov is well known for.

2) The key difference is what the training data look like.

In a supervised learning problem, the training data are input/output pairs (a stimulus and an appropriate action).

In reinforcement learning, the training data are input and reward pairs (an action and the reward applied to that action).

I would argue that Pavlov's experiments more like the latter case - the dogs are not shown 'this is the correct action for this stimulus', they are shown 'this is the reward for this stimulus'.