Improving Generalisation for Temporal Difference Learning:
The Successor Representation.

Peter Dayan
Neural Computation, 5, 613-624.


Abstract

Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes particular constraints on good function approximators or representations. Appropriate generalisation between states is determined by how similar their successors are, and representations should follow suit. This paper shows how TD machinery can be used to learn such representations, and illustrates, using a navigation task, the appropriately distributed nature of the result.
compressed postscript   pdf

back to:   top     publications