Deictic option schemas
Balaraman Ravindran, Andrew G. Barto, Vimal Mathew · International Joint Conference on Artificial Intelligence · 2007
Deictic representation is a representational paradigm, based on selective attention and pointers, that allows an agent to learn and reason about rich complex environments. In this article we present a hierarchical reinforcement learning framework that employs aspects of deictic representation. We also present a Bayesian algorithm for learning the correct representation for a given sub-problem and empirically validate it on a complex game environment.