Off-policy evaluation in Reinforcement Learning using Linear Regression



Friday, 11 December 2020, 17:15 to 18:15


In Reinforcement Learning, one often needs to evaluate a given policy using rewards observed by following another policy. This is called off-policy evaluation in Learning Theory parlance. The traditional methods for off-policy evaluation involve importance sampling, which comes with certain drawbacks. We shall look at these drawbacks and how linear regression may be used instead to overcome the same.

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