Applying “reinforcement learning” on a supervised learning model

Question

Is it possible to use "reinforcement learning" or a feedback loop on a supervised model?

I have worked on a machine learning problem using a supervised learning model, more precisely a linear regression model, but I would like to improve the results by creating a feedback loop on the outputs of the prediction, ie, tell the algorithm if it made mistakes on some examples.

As I know, this is basically how reinforcement learning works: the model learns from positive and negative feedbacks.

I found out that we can implement supervised learning and reinforcement learning algorithms using PyBrain , but I couldn't find a way to relate between both.

Answer 1

Most (or maybe all) iterative supervised learning methods already use a feedback loop on the outputs of the prediction. If fact, this feedback is very informative since it provides information with the exact amount of error in each sample. Think for example in stochastic gradient descent, where you compute the error of each sample to update the model parameters.

In reinforcement learning the feedback signal (ie, reward) is much more limited than in supervised learning. Therefore, in the typical setup of adjusting some model parameters, if you have a set of input-output (ie, a training data set), probably it has no sense to apply reinforcement learning.

If you are thinking on a more specific case/problem, you should be more specific in your question.

Answer 2

Reinforcement Learning has been used to tune hyper-parameters and/or select optimal Supervised Learning Models. There's also a paper on it: "Learning to optimize with Reinforcement Learning".

Reading Pablo's answer you may want to read up on "back propagation". It may be what you are looking for.