Temporal Abstraction in Temporal-difference Networks

The primary distinguishing feature of temporal-difference (TD) networks (Sutton & Tanner, 2005) is that they permit a general compositional specification of the goals of learning. The goals of learning are thought of as predictive questions being asked by the agent in the learning problem, such as "What will I see if I step forward and look right?" or "If I open the fridge, will I see a bottle of beer?" Seeing a bottle of beer is of course a complicated perceptual act. It might be thought of as obtaining a set of predictions about what would happen if certain reaching and grasping actions were taken, about what would happen if the bottle were opened and turned upside down, and of what the bottle would look like if viewed from various angles. To predict seeing a bottle of beer is thus to make a prediction about a set of other predictions. The target for the overall prediction is a composition in the mathematical sense of the first prediction with each of the other predictions. TD networks are the first framework for representing the goals of predictive learning in a compositional, machine-accessible form. Each node of a TD network represents an individual question--something to be predicted--and has associated with it a value representing an answer to the question--a prediction of that something. The questions are represented by a set of directed links between nodes.