Composing functions to speed up reinforcement learning in a changing world

Abstract

This paper presents a system that transfers the results of prior learning to speed up reinforcement learning in a changing world. Often, even when the change to the world is relatively small an extensive relearning effort is required. The new system exploits strong features in the multi-dimensional function produced by reinforcement learning. The features generate a partitioning of the state space. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. The experimental results investigate one important example of a changing world, a new goal position. In this situation, there is close to a two orders of magnitude increase in learning rate over using a basic reinforcement learning algorithm.