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.
A cosine learning- rate schedule with linear warmup over the first10 4 steps is applied, starting from an initial learning rate of5×10 −5
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.