  Agents trained with DQN rely on an observation at each timestep to decide
what action to take next. However, in real world applications observations can
change or be missing entirely. Examples of this could be a light bulb breaking
down, or the wallpaper in a certain room changing. While these situations
change the actual observation, the underlying optimal policy does not change.
Because of this we want our agent to continue taking actions until it receives
a (recognized) observation again. To achieve this we introduce a combination of
a neural network architecture that uses hidden representations of the
observations and a novel n-step loss function. Our implementation is able to
withstand location based blindness stretches longer than the ones it was
trained on, and therefore shows robustness to temporary blindness. For access
to our implementation, please email Nathan, Marije, or Pau.
