Reinforcement Learning for Adaptive Recommendation of Bioactive Peptide Combinations in Healthy Aging

Abstract

Healthy aging is increasingly framed as a systems problem in which nutrition, biology, behavior, clinical monitoring, and digital infrastructure interact over long time horizons. Bioactive peptides have attracted attention for their potential roles in metabolic regulation, immune modulation, musculoskeletal maintenance, and age-related disease risk reduction, yet the selection of peptide combinations remains difficult because effects are context-dependent, evidence is uneven, and individual responses vary. This paper examines reinforcement learning as an adaptive recommendation layer for bioactive peptide combinations in healthy aging. Rather than treating recommendation as a one-time classification task, the discussion positions reinforcement learning as a sequential decision system that can learn from longitudinal outcomes, safety constraints, and user context. The paper develops a system-level analysis of architecture, data infrastructure, reward design, robustness, fairness, governance, sustainability, and policy. It argues that the central challenge is not merely algorithmic performance but the construction of a socio-technical infrastructure capable of supporting safe, interpretable, continuously monitored, and equitable recommendations. Particular attention is given to structural trade-offs between personalization and population-level evidence, exploration and safety, automation and clinical oversight, and commercial deployment and public accountability. The paper concludes that adaptive peptide recommendation should be governed as a high-consequence health-adjacent system, with validated endpoints, auditability, human oversight, and lifecycle evaluation. If designed carefully, reinforcement learning could support more personalized and responsive healthy aging interventions; if designed poorly, it could amplify weak evidence, obscure uncertainty, and produce inequitable outcomes.