Deictic Option Schemas

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


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