Deictic Option Schemas

Ravindran, Balaraman; Barto, Andrew G.; Mathew, Vimal

Deictic Option Schemas

Balaraman Ravindran, Andrew G. Barto, Vimal Mathew

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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.

Balaraman Ravindran, Andrew G. Barto, and Vimal Mathew. 2007. Deictic Option Schemas. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 1023–1027. https://ijcai.org/proceedings/2007/165

@inproceedings{ijcai2007p165,
  title     = {Deictic Option Schemas},
  author    = {Ravindran, Balaraman and Barto, Andrew G. and Mathew, Vimal},
  booktitle = {Proceedings of the Twentieth International Joint Conference on
               Artificial Intelligence, {IJCAI-07}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Ramon López de Mántaras},
  pages     = {1023--1027},
  year      = {2007},
  url       = {https://ijcai.org/proceedings/2007/165},
}

TY  - CPAPER
TI  - Deictic Option Schemas
AU  - Ravindran, Balaraman
AU  - Barto, Andrew G.
AU  - Mathew, Vimal
T2  - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2007
SP  - 1023
EP  - 1027
UR  - https://ijcai.org/proceedings/2007/165
ER  -