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From your question I assume that you are familiar with at least basic concepts in RL so I won't dive into too many details. RL in general is not SGD. In RL you will encounter various optimization schemes in order to optimize an utility function. Two of the most popular families of methods used for optimizing an utility function (in RL MDP formulation) are ...


Reinforcement Learning is not an optimization algorithm (which stochastic gradient descent is). Stochastic Gradient Descent is an optimization algorithm which seeks to minimize a given target/objective function. Reinforcement learning does nothing of that sort. Reinforcement Learning is essentially learning the parameters of a Markov Decision Process (MDP). ...


A quick answer: is that solving the dual problem is, in many practical cases, easier to solve (more tractable algorithms exist), than the primal problem and leads to the solution of the primal problem as well. Exception to this, is Linear SVMs, where the primal problem is equally easy. Note: The primal/dual is a general method that can be used in many areas ...


I would try to use a genetic algorithm. A simple representation of the problem in terms of a genetic algorithm would go like this: A "gene" represents one of the parameters. An "individual" consists of assigning a value to each parameter. An "individual" represents a candidate solution, and it can be evaluated using the ...


You could also try a linear SVM and examine the weights. Sort the features by resulting weights to get a view of which ones are more important. Or, just run a statistical logistic regression model and produce p-values for each feature. A quick search shows that the statsmodel package will do this for you.


One option is a decision tree.

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