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Transfer learning for reinforcement learning through goal and policy parametrization

Relational reinforcement learning has allowed results from reinforcement learning tasks to be re-used in other, closely related, tasks. This transfer of knowledge is made possible by the use of parameters in the representations of the task-description and the learned policy. In this paper, we will give a description of the current state of the art of transfer learning with relational reinforcement learning, make some observations about the usefulness and limitations of this current state and discuss some directions for future research. We also present a first small step along one of those directions.