Distributionally robust policy learning focuses on finding a policy that performs well even under the worst-case distributional shift.Existing methods consider the worst-case joint distribution, which can be overly conservative.This study addresses robust policy learning under concept drifts, where the conditional relationship between outcome and covariate changes.Proposed methods include a doubly-robust estimator and a learning algorithm for maximizing policy value, showing improvement over existing benchmarks.