Reinforcement Learning Web Crawler Navigation
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Solution Overview
Problem
Current technologies lack effective utilization of reinforcement learning techniques for extracting meaningful insights from web data, limiting their application in areas like risk assessment and marketing.
Innovation Solution
A reinforcement learning system that includes an agent and interpreter working with a web crawler to identify and perform goal-oriented actions on webpages, using classifiers and state information to determine optimal actions and provide rewards for maximizing rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If reinforcement learning techniques are applied to web data extraction, then meaningful insights for risk assessment and marketing can be obtained, but the system complexity increases compared to traditional web crawling
Solution Approach 1:
The patent introduces an interpreter as an intermediary component that bridges the reinforcement learning agent and the web crawler. The interpreter translates the agent's abstract actions into concrete crawler operations, managing system complexity while enabling sophisticated web data extraction through reinforcement learning
2Productivity
If goal-oriented navigation with reinforcement learning is implemented, then data extraction efficiency improves, but the difficulty of detecting and measuring system state increases
Solution Approach 1:
The patent implements a feedback mechanism where the interpreter continuously monitors the web crawler's state and provides this information back to the reinforcement learning agent. This feedback loop enables the agent to adjust its actions based on current system state, improving data extraction efficiency while maintaining manageable state detection through structured observation
3Measurement precision
If classifiers and state information are used to determine optimal actions, then navigation accuracy improves, but computational requirements increase
Solution Approach 1:
The patent applies partial action by using classifiers selectively to evaluate only the most relevant features of web pages and crawler states. Rather than analyzing all possible state parameters, the system focuses on key discriminative features, achieving high navigation accuracy while reducing computational overhead
Data Source
AI summary
A computer system receives a goal for an environment, wherein the environment corresponds to at least one webpage. The computer system receives one or more classifiers corresponding to the environment, wherein the one or more classifiers provide information corresponding to a current webpage and information corresponding to one or more previous actions taken by a web crawler. The computer system identifies a recommended next action based on the one or more classifiers. The computer system transmits the recommended next action to the web crawler to cause the web crawler to perform the recommended next action.