  Computer simulation provides an automatic and safe way for training robotic
control policies to achieve complex tasks such as locomotion. However, a policy
trained in simulation usually does not transfer directly to the real hardware
due to the differences between the two environments. Transfer learning using
domain randomization is a promising approach, but it usually assumes that the
target environment is close to the distribution of the training environments,
thus relying heavily on accurate system identification. In this paper, we
present a different approach that leverages domain randomization for
transferring control policies to unknown environments. The key idea that,
instead of learning a single policy in the simulation, we simultaneously learn
a family of policies that exhibit different behaviors. When tested in the
target environment, we directly search for the best policy in the family based
on the task performance, without the need to identify the dynamic parameters.
We evaluate our method on five simulated robotic control problems with
different discrepancies in the training and testing environment and demonstrate
that our method can overcome larger modeling errors compared to training a
robust policy or an adaptive policy.
