*[NOTE: the slides are not visible for the first five minutes of the talk! Audio was captured correctly for the whole duration of the talk.]* Reinforcement learning (RL) is increasingly used to discover control strategies for complex physical systems, yet turbulent flows remain a challenge: the dynamics are high-dimensional and nonlinear, sensing is sparse and noisy, high-fidelity simulations are computationally intractable, and experiments are expensive. In this talk, I will present recent work aimed at making RL practical for real-world flow control by combining sample-efficient learning with physics-aware representations of partially observable dynamics inherent to realistic sensor configurations.