Technical implementation of real-time strategy adjustment
The system innovatively applies the policy gradient method (PPO algorithm) in reinforcement learning to research process optimization. When the confidence level of the initial search results is below a threshold, it triggers the policy network to generate a new search scheme. The technical white paper discloses that the system adopts a layered reinforcement learning architecture: the upper network is responsible for the research framework design (e.g., problem disassembly order), and the lower network controls the specific operations (e.g., keyword optimization).
A typical case shows that when researching 'AI in healthcare', the system optimizes the query to 'AI medical image diagnosis latest technology 2024' after 3 iterations, and the related literature match is improved from the initial 47% to 89%. all strategy adjustment records are saved in . /outputs directory in a JSON file containing the complete decision tree and revenue evaluation data.
This answer comes from the articleDeepResearcher: driving AI to study complex problems based on reinforcement learningThe
English 
简体中文 
日本語 
Deutsch 
Português do Brasil