Data center siting is locked in for years: one multi-objective RL agent lets planners retune water, power, carbon and reliability at inference time
AI data center siting, power procurement and cooling are still settled once by static single-point scoring on day-zero numbers, though water, power and carbon drift over a 10- to 20-year asset life and commitments bind for years. The build-out becomes a tiered Markov decision process: strategic commitments occur only in discrete windows, carry switching costs and take effect after source-dependent lead times, dispatch uses only capacity that exists, and a preference-conditioned multi-objective agent returns a policy family steerable at inference time across cost, carbon, water and reliability. On real site data, preference conditioning gives a confirmed, steerable trade-off on both objective pairs across three commitment regimes, and lock-in covers the frontier more evenly.