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Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo searchalgorithm for optimizing a sequence of choices. We propose to improve on GNRPAby avoiding too deterministic policies that find again and again the samesequence of choices. We do so by limiting the number of repetitions of the bestsequence found at a given level. Experiments show that it improves thealgorithm for three different combinatorial problems: Inverse RNA Folding, theTraveling Salesman Problem with Time Windows and the Weak Schur problem.

Generalized Nested Rollout Policy Adaptation with Limited Repetitions