Transfer Learning through Indirect Encoding

摘要

An important goal for the generative and developmental systems (GDS) community is to show that GDS approaches can compete with more mainstream approaches in machine learning (ML). One popular ML domain is RoboCup and its subtasks (e.g. Keepaway). This paper shows how a GDS approach called HyperNEAT competes with the best results to date in Keepaway. Furthermore, a significant advantage of GDS is shown to be in transfer learning. For example, playing Keepaway should contribute to learning the full game of soccer. Previous approaches to transfer have focused on transforming the original representation to fit the new task. In contrast, this paper explores transfer with a representation designed to be the same even across different tasks. A bird's eye view (BEV) representation is introduced that can represent different tasks on the same two-dimensional map. Yet the problem is that a raw two-dimensional map is high-dimensional and unstructured. The problem is addressed naturally by indirect encoding, which compresses the representation in HyperNEAT by exploiting its geometry. The result is that the BEV learns a Keepaway policy that transfers from two different training domains without further learning or manipulation. The results in this paper thus show the power of GDS versus other ML methods.