A three-agent reinforcement learning framework with domain-informed surrogate rewards outperforms single-agent and random policies for personalizing dyadic medication-adherence interventions in simulation.
John Wiley & Sons (1987)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Reinforcement Learning on Dyads to Enhance Medication Adherence
A three-agent reinforcement learning framework with domain-informed surrogate rewards outperforms single-agent and random policies for personalizing dyadic medication-adherence interventions in simulation.