Reinforcement Learning Agent Training via Batched Reward Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Reinforcement learning processes in computer processing require a large amount of data and learning can be slow, making it inefficient for real-time applications.
Innovation Solution
A computer-implemented system and method for training an automated agent using a reinforcement learning neural network, which processes task data to compute performance metrics and generates rewards based on differences in these metrics across time intervals, to efficiently train the agent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reinforcement learning processes use traditional training methods with large amounts of data, then learning accuracy is improved, but learning speed deteriorates
Solution Approach 1:
The patent segments the training data into multiple batches and processes them in parallel across multiple computing devices. Each device handles a portion of the data independently, allowing simultaneous processing that accelerates training while maintaining accuracy through collective results.
Solution Approach 2:
The system performs preliminary data preprocessing and feature extraction before the main training process. Data is prepared, normalized, and organized in advance, reducing the computational burden during actual training and enabling faster convergence without sacrificing learning quality.
2Reliability
If reinforcement learning processes process large amounts of data, then model performance is improved, but processing time deteriorates
Solution Approach 1:
The patent divides the large dataset into smaller batches and distributes processing across multiple computing devices. Each device processes a segment independently and simultaneously, reducing the time required to process the entire dataset while maintaining model performance through aggregated learning results.
Solution Approach 2:
The system implements periodic training cycles with alternating phases of data processing and model updating. Training occurs in periodic batches rather than continuously processing all data at once, enabling efficient use of computational resources and faster overall processing while maintaining performance through iterative improvement.
3Measurement precision
If reinforcement learning uses comprehensive task data from multiple entities, then performance metric accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent merges data from multiple entities into a unified training dataset with standardized formats. By combining heterogeneous data sources into a consistent structure, the system achieves accurate performance metrics across entities while reducing the complexity of handling separate data processing pipelines for each entity.
Data Source
AI summary
Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.