  While current benchmark reinforcement learning (RL) tasks have been useful to
drive progress in the field, they are in many ways poor substitutes for
learning with real-world data. By testing increasingly complex RL algorithms on
low-complexity simulation environments, we often end up with brittle RL
policies that generalize poorly beyond the very specific domain. To combat
this, we propose three new families of benchmark RL domains that contain some
of the complexity of the natural world, while still supporting fast and
extensive data acquisition. The proposed domains also permit a characterization
of generalization through fair train/test separation, and easy comparison and
replication of results. Through this work, we challenge the RL research
community to develop more robust algorithms that meet high standards of
evaluation.
