Reinforcement Learning Agent for Data Retrieval
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Solution Overview
Problem
Existing data retrieval methods, such as manual entry and data scraping, are time-consuming, prone to errors, and inefficient due to lack of optimization for individual data sources, and existing search algorithms fail to utilize prior knowledge about data sources, leading to missed or irrelevant data.
Innovation Solution
The use of reinforcement learning techniques, where a reinforcement learning agent is trained with intermediate rewards to efficiently navigate and retrieve data from diverse data sources by assigning rewards based on the occurrence of values in training episodes, allowing for faster convergence and accurate data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data retrieval methods are used, then data can be entered and processed, but the process is time-consuming and prone to human errors
Solution Approach 1:
The patent replaces manual mechanical data entry with an automated reinforcement learning agent that navigates data sources and extracts information. The agent learns optimal navigation strategies through training episodes, substituting human operators with an intelligent system that operates faster and with higher accuracy.
Solution Approach 2:
The reinforcement learning agent autonomously navigates data sources, makes decisions about where to extract data, and learns from its own experiences through reward signals. The system serves itself by continuously improving its navigation policy without requiring manual reconfiguration for different data sources.
2Productivity
If existing data scraping methods are used, then data can be extracted, but the methods are not optimized for individual data sources resulting in missed or irrelevant records
Solution Approach 1:
The patent applies local quality by customizing the data extraction approach for each specific data source. The reinforcement learning agent learns source-specific navigation patterns and characteristics, adapting its behavior to the unique structure and features of each data source rather than applying a uniform scraping method.
Solution Approach 2:
The system changes parameters by adjusting the navigation policy and extraction strategy based on the specific characteristics of each data source. The reinforcement learning agent learns optimal parameters for navigating different data sources, including navigation paths, selection criteria, and extraction timing.
3Ease of operation
If existing search algorithms are used, then data locations can be searched, but the algorithms lack prior knowledge about data source structures leading to inefficiency
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning agent on multiple training episodes that capture the structure and characteristics of data sources. This preliminary training equips the agent with prior knowledge about data source structures before actual data retrieval begins, enabling more efficient searches.
Solution Approach 2:
The system implements feedback through reward signals that guide the reinforcement learning agent's navigation decisions. The agent receives feedback about successful data retrievals and uses this information to improve its navigation policy, learning from past experiences to become more efficient at locating data.
Data Source
AI summary
The present disclosure provides techniques for data retrieval using machine learning. One example method includes receiving a plurality of training episodes associated with different environments, wherein each training episode of the plurality of training episodes includes a sequence of states, computing, based on the plurality of training episodes, total counts of a plurality of values in the states, initializing, for each state of the sequence of states in each training episode of the plurality of training episodes, a reward based on the total counts of the plurality of values, and training a reinforcement learning agent using the rewards.