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Despite recent progress in Reinforcement Learning for robotics applications,many tasks remain prohibitively difficult to solve because of the expensiveinteraction cost. Transfer learning helps reduce the training time in thetarget domain by transferring knowledge learned in a source domain. Sim2Realtransfer helps transfer knowledge from a simulated robotic domain to a physicaltarget domain. Knowledge transfer reduces the time required to train a task inthe physical world, where the cost of interactions is high. However, mostexisting approaches assume exact correspondence in the task structure and thephysical properties of the two domains. This work proposes a framework forFew-Shot Policy Transfer between two domains through Observation Mapping andBehavior Cloning. We use Generative Adversarial Networks (GANs) along with acycle-consistency loss to map the observations between the source and targetdomains and later use this learned mapping to clone the successful source taskbehavior policy to the target domain. We observe successful behavior policytransfer with limited target task interactions and in cases where the sourceand target task are semantically dissimilar.

A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning