  Research in deep reinforcement learning (RL) has coalesced around improving
performance on benchmarks like the Arcade Learning Environment. However, these
benchmarks conspicuously miss important characteristics like abrupt
context-dependent shifts in strategy and temporal sensitivity that are often
present in real-world domains. As a result, RL research has not focused on
these challenges, resulting in algorithms which do not understand critical
changes in context, and have little notion of real world time. To tackle this
issue, this paper introduces the game of Space Fortress as a RL benchmark which
incorporates these characteristics. We show that existing state-of-the-art RL
algorithms are unable to learn to play the Space Fortress game. We then confirm
that this poor performance is due to the RL algorithms' context insensitivity
and reward sparsity. We also identify independent axes along which to vary
context and temporal sensitivity, allowing Space Fortress to be used as a
testbed for understanding both characteristics in combination and also in
isolation. We release Space Fortress as an open-source Gym environment.
