US 2025/0053823 A1 · SYSTEMS AND METHODS FOR TRAINING REINFORCEMENT LEARNING MODELS USING UNSUPERVISED MODELS

Abstract

Methods and systems for training a reinforcement learning model using training data generated using an unsupervised model. In some aspects, the system processes a first unlabeled dataset using an unsupervised model to generate a first set of labels associated with statistical properties of the first unlabeled dataset. The system generates a labeled training dataset using the first set of labels and the first unlabeled dataset. The system uses the labeled training dataset to train a reinforcement learning model to identify abnormalities and changes to statistical distributions within data. The system uses the reinforcement learning model to process a second unlabeled dataset to generate a second set of labels associated with statistical properties of the second unlabeled dataset.

Claims20

Dependent claims

A system for training a reinforcement learning model using training data generated using an unsupervised model, comprising:

one or more processors;
and
one or more non-transitory, computer-readable media comprising instructions that, when executed by the one or more processors, cause operations comprising:
receiving a first time-series dataset;
processing the first time-series dataset using an unsupervised model to generate a set of changepoints in the first time-series dataset;
using the first time-series dataset and the set of changepoints, generating a first training dataset;
using the first training dataset, training a reinforcement learning model to identify changepoints in time-series data;
and
using the reinforcement learning model, processing a second time-series dataset to generate one or more notifications comprising changepoints in the second time-series dataset.

6 more elements

A method for training a reinforcement learning model using training data generated using an unsupervised model, the method comprising:

processing a first unlabeled dataset using an unsupervised model to generate a first set of labels associated with statistical properties of the first unlabeled dataset;
using the first set of labels and the first unlabeled dataset, generating a labeled training dataset;
using the labeled training dataset, training a reinforcement learning model to identify abnormalities and changes to statistical distributions within data;
and
using the reinforcement learning model, processing a second unlabeled dataset to generate a second set of labels associated with statistical properties of the second unlabeled dataset.

2 more elements

One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors, cause operations comprising:

processing a first unlabeled dataset using a first machine learning model to generate a first set of labels associated with statistical properties of the first unlabeled dataset;
using the first set of labels and the first unlabeled dataset, generating a labeled training dataset;
using the labeled training dataset, training a second machine learning model to identify abnormalities and changes to statistical distributions within data;
and
using the second machine learning model, processing a second unlabeled dataset to generate a second set of labels associated with statistical properties of the second unlabeled dataset.

2 more elements

Dependencies are inferred from the claim text.

Systems and methods for training reinforcement learning models using unsupervised models

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Systems and methods for training reinforcement learning models using unsupervised models

Life of this patent

Abstract

Claims20