title: Policy Transfer with Strategy Optimization
abstract: 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.
authors: Wenhao Yu C. Karen Liu Greg Turk
hIndex: Greg Turk 51