Evolutionarily-Curated Curriculum Learning for Deep Reinforcement
  Learning Agents
  In this paper we propose a new training loop for deep reinforcement learning
agents with an evolutionary generator. Evolutionary procedural content
generation has been used in the creation of maps and levels for games before.
Our system incorporates an evolutionary map generator to construct a training
curriculum that is evolved to maximize loss within the state-of-the-art Double
Dueling Deep Q Network architecture with prioritized replay. We present a
case-study in which we prove the efficacy of our new method on a game with a
discrete, large action space we made called Attackers and Defenders. Our
results demonstrate that training on an evolutionarily-curated curriculum
(directed sampling) of maps both expedites training and improves generalization
when compared to a network trained on an undirected sampling of maps.
