Learning Time-Sensitive Strategies in Space Fortress
  Although there has been remarkable progress and impressive performance on
reinforcement learning (RL) on Atari games, there are many problems with
challenging characteristics that have not yet been explored in Deep Learning
for RL. These include reward sparsity, abrupt context-dependent reversals of
strategy and time-sensitive game play. In this paper, we present Space
Fortress, a game that incorporates all these characteristics and experimentally
show that the presence of any of these renders state of the art Deep RL
algorithms incapable of learning. Then, we present our enhancements to an
existing algorithm and show big performance increases through each enhancement
through an ablation study. We discuss how each of these enhancements was able
to help and also argue that appropriate transfer learning boosts performance.
