  Much of the previous work towards digital agents for graphical user
interfaces (GUIs) has relied on text-based representations (derived from HTML
or other structured data sources), which are not always readily available.
These input representations have been often coupled with custom, task-specific
action spaces. This paper focuses on creating agents that interact with the
digital world using the same conceptual interface that humans commonly use --
via pixel-based screenshots and a generic action space corresponding to
keyboard and mouse actions. Building upon recent progress in pixel-based
pretraining, we show, for the first time, that it is possible for such agents
to outperform human crowdworkers on the MiniWob++ benchmark of GUI-based
instruction following tasks.
