Abstract: |
Policy transfer occurs when a system transfers a policy learnt for one task to another task with little or no retraining, and allows a system to perform robustly and learn efficiently, especially when the new task is more complex than the original task. In this paper we report on work in progress into policy transfer using a relational learning classifier system. The system, F\textsc{ox-cs}, uses a high level relational language (a subset first order logic) in combination with a $P$-learning technique adapted for X\textsc{cs} and its derivatives. F\textsc{ox-cs} achieved successful policy transfer in two blocks world tasks, \emph{stacking} and \emph{onab}, by learning a policy that was independent of the number of blocks, thus avoiding the prohibitive training times that would normally arise due to the exponential explosion in the number of states as the number of blocks increases. |
