limitedDistribution · Industry Research
Prompt-to-policy: Leveraging large language models to guide deep reinforcement learning in public health emergencies
The article explores a hybrid agent using LLMs and RAG for dynamic decision-making, validated on COVID-19 data, enhancing efficiency and robustness.
Stargo's RAG-powered insights enhance decision-making by processing unstructured data, similar to the hybrid agent's efficiency in public health emergencies.
Executive Summary
The article discusses a hybrid agent that integrates D2QN-JDA with large language models (LLMs) for dynamic decision-making in public health emergencies. LLMs with retrieval-augmented generation (RAG) process multi-source heterogeneous data to calculate adaptive rewards. The D2QN-JDA method is validated on real-world COVID-19 data from Hong Kong, demonstrating superior cost efficiency and robustness compared to other algorithms. The approach automates the processing of heterogeneous, real-time data, reducing manual intervention and enhancing system robustness. LLMs outperform manual and regex methods in data extraction, improving accuracy, recall, F1 score, cost, and time.
Source: www.sciencedirect.com
Authors: X. Wang, D. Duma, M.R. Dihan, P. Chowdhury, L. Thul, J. Liu, W. Xiao, J. Cui, L. Yu, J. Lee, Z. Du, D. Š.emrov, T. Sun, M.D. Stosic, V. Chaudhary, R.M. Anderson, S.L. Wang, K. Huang, J. Zhang, E. Keyvanshokooh, Y. Zhang, Y. Wang, T. Ning, D. Hachiya, J. Fan, Q. Liu, H. Li, H. Zhou
Original Article: https://www.sciencedirect.com/science/article/abs/pii/S016926072600057X
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