  Domain Randomization (DR) is known to require a significant amount of
training data for good performance. We argue that this is due to DR's strategy
of random data generation using a uniform distribution over simulation
parameters, as a result, DR often generates samples which are uninformative for
the learner. In this work, we theoretically analyze DR using ideas from
multi-source domain adaptation. Based on our findings, we propose Adversarial
Domain Randomization (ADR) as an efficient variant of DR which generates
adversarial samples with respect to the learner during training. We implement
ADR as a policy whose action space is the quantized simulation parameter space.
At each iteration, the policy's action generates labeled data and the reward is
set as negative of learner's loss on this data. As a result, we observe ADR
frequently generates novel samples for the learner like truncated and occluded
objects for object detection and confusing classes for image classification. We
perform evaluations on datasets like CLEVR, Syn2Real, and VIRAT for various
tasks where we demonstrate that ADR outperforms DR by generating fewer data
samples.
