Importance

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Abstract

Techniques described herein include selecting experience data for use when training or retraining a model. In one example, this disclosure describes a method that includes generating a plurality of trajectories, each comprising a contiguous sequence of instances of experience data, where each instance of experience data in the contiguous sequence has an error value associated that instance of experience data; determining, for each of the trajectories, a sorted order of the instances of experience data, wherein the sorted order is based on the error value associated with each of the instances of experience data; selecting, based on a distribution function applied to the sorted order of the instances of experience data in at least one of the trajectories, a subset of instances of the experience data; and retraining a reinforcement learning model, using the subset of instances of experience data, to predict an optimal action to take in a state.

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First Claim