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.
We propose a policy transfer algorithm that can overcome large and challenging discrepancies in the system dynamics such as latency, actuator modeling error, etc.