  This paper introduces Dex, a reinforcement learning environment toolkit
specialized for training and evaluation of continual learning methods as well
as general reinforcement learning problems. We also present the novel continual
learning method of incremental learning, where a challenging environment is
solved using optimal weight initialization learned from first solving a similar
easier environment. We show that incremental learning can produce vastly
superior results than standard methods by providing a strong baseline method
across ten Dex environments. We finally develop a saliency method for
qualitative analysis of reinforcement learning, which shows the impact
incremental learning has on network attention.
