Skill Transfer for Mediated Interaction Learning

We investigate the effectiveness of transfer learning for accelerating shallow and deep reinforcement learning of “mediated interaction tasks”. In these tasks, the desired effects cannot be created through direct interaction, but instead require the learner to discover how to exert suitable effects on the target object through involving a suitable “mediator object”. We focus on the case where transfer learning is applied to generalize experiences from source tasks that are solvable through direct, unmediated interaction, to target tasks that require mediated interaction for their solution. We find that transfer learning employed in this context leads to a significant acceleration of learning. A refinement of the basic transfer learning strategy that is motivated from the principle of scaffolding in psychology leads to further improvements, totalling to an overall speed-up factor of almost one order of magnitude for a reinforcement learner solving an “extension-of-reach” task in a 2D world with simulated physics.