Reinforcement Learning Model Training via Unsupervised Data Augmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reinforcement learning models often perform sub-optimally due to scarcity and quality issues of training data, which existing systems have not adequately addressed by using unsupervised learning models to generate or supplement training data.
Innovation Solution
The system employs an unsupervised learning model to process unlabeled datasets, generate labels, and create a labeled training dataset, which is then used to train a reinforcement learning model to identify abnormalities and changes in statistical distributions within input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional workflows are used for training reinforcement learning models, then the model training process is simple, but the training data is scarce and low quality resulting in sub-optimal model performance
Solution Approach 1:
The patent introduces an unsupervised learning model as an intermediary component that processes unlabeled data and generates synthetic training data. This intermediary transforms the scarce and low-quality training data problem into a solution by creating artificial training examples that augment the limited real data, thereby improving reinforcement learning model performance without requiring additional high-quality labeled data
Solution Approach 2:
The system creates synthetic training data that copies and transforms unlabeled data through the unsupervised learning model. This copying process generates artificial training examples that replicate the structure and patterns of real data while augmenting the dataset, effectively increasing both the quantity and quality of training data available for reinforcement learning model training
2Reliability
If unsupervised learning models are used to generate training data, then training data quality improves, but the system complexity increases
Solution Approach 1:
The patent segments the data processing system into distinct functional components: an unsupervised learning model for processing unlabeled data, a data generation module for creating synthetic training data, and a reinforcement learning model for final training. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by dividing complex data processing tasks into manageable modules
Data Source
AI summary
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.