A Relational Instance-Based Policy can be defined as an action policy described following a relational instancebased learning approach. The policy is represented with a set of state-goal-action tuples in some form of predicate logic and a distance metric: whenever the planner is in a state trying to reach a goal, the next action to execute is computed as the action associated to the closest state-goal pair in that set. In this work, the representation language is relational, following the ideas of Relational Reinforcement Learning. The policy to transfer (the set of state-goal-action tuples) is generated with a planning system solving optimally simple source problems. The target problems are defined in the same planning domain, have different initial and goal states to the source problems, and could be much more complex. We show that the transferred policy can solve similar problems to the ones used to learn it, but also more complex problems. In fact, the policy learned outperforms the planning system used to generate the initial state-action pairs in two ways: it is faster and scales up better.

Abstract. The application of learning approaches as Kernel or Instance Based methods to tree structured data requires the definition of similarity functions able to deal with such data. A new similarity function for nearest prototype classification in relational data that follows a tree structure is defined in this paper. Its main characteristic is its capability to weight the importance of the different data features in different areas of the feature space. This work is built over two previous ideas: a similarity function for Local Feature Weighting (LFW), and a Relational Nearest Prototype Classification algorithm (RNPC). 1