Exploration and Inference in Learning from Reinforcement

Abstract

Recently there has been a good deal of interest in using techniques developed for learning from reinforcement to guide learning in robots. Motivated by the desire to find better robot learning methods, this thesis presents a number of novel extensions to existing techniques for controlling exploration and inference in reinforcement learning. First I distinguish between the well known exploration-exploitation trade-off and what I term exploration for future exploitation. It is argued that there are many tasks where it is more appropriate to maximise this latter measure. In particular it is appropriate when we want to employ learning algorithms as part of the process of designing a controller. Informed by this insight I develop a number of novel measures of the agent's task knowledge. The first of these is a measure of the probability of a particular course of action being the optimal course of action. Estimators are developed for this measure for boolean and non-boolean processes. These...