A Framework for Few-Shot Policy Transfer through Observation Mapping and
  Behavior Cloning
Yash Shukla Bharat Kesari Shivam Goel Robert Wright Jivko Sinapov
Despite recent progress in Reinforcement Learning for robotics applications,
many tasks remain prohibitively difficult to solve because of the expensive
interaction cost. Transfer learning helps reduce the training time in the
target domain by transferring knowledge learned in a source domain. Sim2Real
transfer helps transfer knowledge from a simulated robotic domain to a physical
target domain. Knowledge transfer reduces the time required to train a task in
the physical world, where the cost of interactions is high. However, most
existing approaches assume exact correspondence in the task structure and the
physical properties of the two domains. This work proposes a framework for
Few-Shot Policy Transfer between two domains through Observation Mapping and
Behavior Cloning. We use Generative Adversarial Networks (GANs) along with a
cycle-consistency loss to map the observations between the source and target
domains and later use this learned mapping to clone the successful source task
behavior policy to the target domain. We observe successful behavior policy
transfer with limited target task interactions and in cases where the source
and target task are semantically dissimilar.