Running experiments under constraints? This method adapts as it learns.

Bayesian experimental design typically assumes you can run any experiment you want. Reality is messier: budgets run out, costs vary, physical limits shift. This work trains a neural policy offline, then uses multi-step lookahead planning at runtime to adapt to changing constraints. The result: substantially more informative experiment sequences than existing approaches, without prohibitive computational slowdown.