  In this work we present a method for leveraging data from one source to learn
how to do multiple new tasks. Task transfer is achieved using a self-model that
encapsulates the dynamics of a system and serves as an environment for
reinforcement learning. To study this approach, we train a self-models on
various robot morphologies, using randomly sampled actions. Using a self-model,
an initial state and corresponding actions, we can predict the next state. This
predictive self-model is then used by a standard reinforcement learning
algorithm to accomplish tasks without ever seeing a state from the "real"
environment. These trained policies allow the robots to successfully achieve
their goals in the "real" environment. We demonstrate that not only is training
on the self-model far more data efficient than learning even a single task, but
also that it allows for learning new tasks without necessitating any additional
data collection, essentially allowing zero-shot learning of new tasks.
