Linear dynamical systems are a continuous subclass of reinforcement learning models that are widely used in robotics, finance, engineering, and meteorology. Classical control, since the work of Kalman, has focused on dynamics with Gaussian i.i.d. noise, quadratic loss functions and, in terms of provably efficient algorithms, known systems and observed state. We'll discuss how to apply new machine learning methods which relax all of the above: efficient control with adversarial noise, general loss functions, unknown systems, and partial observation. Based on a series of works with Naman Agarwal, Nataly Brukhim, Karan Singh, Sham Kakade, Max Simchowitz, Cyril Zhang, Paula Gradu, John Hallman