---
title: "Deictic Option Schemas"
authors:
  - "Balaraman Ravindran"
  - "Andrew G. Barto"
  - "Vimal Mathew"
doi: ""
url: "https://ijcai.org/proceedings/2007/165"
pdf: "https://ijcai.org/proceedings/2007/0165.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1023-1027"
track: ""
keywords:
---

# Deictic Option Schemas

Balaraman Ravindran, Andrew G. Barto, Vimal Mathew

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1023-1027.

## Abstract

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.