Balaraman Ravindran, Andrew G. Barto, Vimal Mathew.
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.
Subjects: 12.1 Reinforcement Learning; 3.4 Probabilistic Reasoning
Submitted: Oct 16, 2006