While training with static demonstrations has shown some promise, we show that such methods fall short for controlling real GUIs due to their failure to deal with real world stochasticity and non-stationarity not captured in static observational data.

To tackle these issues, we propose a unified principle of pessimism using distribu-tionally robust Markov decision processes.