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'.
Markov decision processes is an old,
mature, at times deep, and polished
field. Names include R. Bellman,
E. Dynkin, R. Rockafellar, D. Bertsekas.
There are connections with scenario
aggregation, potentials, linear-quadratic-Gaussian
certainty equivalence, currents
of sigma algebras, the strong Markov
property, stopping times, and much more.
Can we be more clear on just what the
Markov processes involved actually are
and, then, how they are to be used?
All I saw was the Github page of gibberish --
I don't use Github whatever the heck it is.
But your URL was fine.
So, the work is a relatively routine
application of classic work from
optimization going way back, e.g.,
to Bellman.
The "Reinforcement learning"
terminology looks like
a new label for some quite
ancient wine.
I've wondered what machine learning
had that was good and new, and so
far I've seen some that is
good but not new and some that is
new but not good.
For an application, it would be
good to justify the Markov
assumption, that is, that
the past and future of the process
are conditionally independent
given the present.
For a more detailed treatment, I'd
recommend, say,
E. B. Dynkin and
A. A. Yushkevich,
'Controlled Markov Processes'.
Hi - in a previous comment you mention a paper you wrote that describes a distribution-free multivariate anomaly detector (this is the comment: https://qht.co/item?id=9580929)
Would you mind emailing me a copy of it please? Address in profile. Thanks in advance!
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'.