PromptPO shows LLMs can act as black-box policy optimizers for sequential RL when leveraging prior knowledge, matching baselines in exploration and robotics but underperforming in MuJoCo.
Reinforcement learning for optimization of covid-19 mitigation policies
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Hierarchical RL with a global cost controller and local marginal-value policies outperforms RMAB and heuristic baselines by 20-30% in simulated multi-cluster SARS-CoV-2 control.
MARL framework for jurisdiction-specific HIV intervention allocation accounting for cross-jurisdictional interactions outperforms single-agent RL in CA/FL simulations under fixed budgets.
citing papers explorer
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When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?
PromptPO shows LLMs can act as black-box policy optimizers for sequential RL when leveraging prior knowledge, matching baselines in exploration and robotics but underperforming in MuJoCo.
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Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning
Hierarchical RL with a global cost controller and local marginal-value policies outperforms RMAB and heuristic baselines by 20-30% in simulated multi-cluster SARS-CoV-2 control.
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A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis
MARL framework for jurisdiction-specific HIV intervention allocation accounting for cross-jurisdictional interactions outperforms single-agent RL in CA/FL simulations under fixed budgets.