Learning Time-Sensitive Strategies in Space Fortress [article]

Akshat Agarwal, Ryan Hope, Katia Sycara
2018 arXiv   pre-print
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
more » ... tate 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.
arXiv:1805.06824v4 fatcat:26lb7tt5cfbcdnfsp4tfprxccq