  While recent progress has spawned very powerful machine learning systems,
those agents remain extremely specialized and fail to transfer the knowledge
they gain to similar yet unseen tasks. In this paper, we study a simple
reinforcement learning problem and focus on learning policies that encode the
proper invariances for generalization to different settings. We evaluate three
potential methods for policy generalization: data augmentation, meta-learning
and adversarial training. We find our data augmentation method to be effective,
and study the potential of meta-learning and adversarial learning as
alternative task-agnostic approaches.
