2
$\begingroup$

One of my friends sent me a problem he was working on lately, and I couldn't help but I wonder how could it be solved using Q-learning. The statement is as follows:

Given the following datasets, the objective is to find a suitable strategy per customer contract to maximize Gain and minimize Cost according to the characteristics of the customer.

train.csv: 5000 independent rows, 33 columns.

Columns description:

  • Day (1, 2 or 3): on which day the strategy was applied.
  • 28 variables (A, B, C, ..., Z, AA, BB): characteristics of the individual;
  • Gain: the gain for this individual for the corresponding strategy;
  • Cost: the cost for this individual for the corresponding strategy;
  • Strategy (0, 1 or 2): the strategy applied on this individual;
  • Success: 1 if the strategy succeeded, 0 otherwise.
  • If Success is 1, then the net gain is Gain - Cost, and if Success is 0, consider a standardized cost of 5.
  1. test.csv: 2 000 independent rows, 31 columns.

Columns description:

  • Index: 0 to 1999, unique for each row.
  • Day (4): on which day the strategy will be applied.
  • 28 variables (A, B, C, ..., Z, AA, BB): characteristics of the client;
  • Gain: the gain for this individual for the corresponding strategy;
  • Cost: the cost for this individual for the corresponding strategy;

From what I understood, the train.csv file is used to build a Q-Learning model, and the test one for generating a strategy and predicting a Success.

My main question is:

How to formulate this problem as an RL problem? How to define an episode? Since the training data is labeled, this could be clearly a classification problem (predicting the strategy), but I have no idea how to solve it using RL (Q-learning ideally). Any ideas will be helpful.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.