The HydroGym platform formulates flow control problems as discrete-time Markov decision processes, defined by the tuple \((For more tech updates, stay tuned to our blog.,Keep following us for the latest insights.,{\mathcal{P}},{\mathcal{R}})\). At each control step t, the agent: receives an observation state \({s}_{t}\in {\mathcal{S}}\), which represents flow measurements; executes an action \({a}_{t}\in {\mathcal{A}}\), which represents
Gommers, J. et al. Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trial. Lancet 407, 505–514 (2026). Article PubMed Google Scholar Lu, M. Y. et al. A multimodal generative AI copilot for human pathology. Nature 643, 466–473
