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Information-guided Planning: An Online Approach for Partially Observable Problems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Forthcoming

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Information-guided Planning: An Online Approach for Partially Observable Problems. / do Carmo Alves, Matheus Aparecido; Varma, Amokh; Soriano Marcolino, Leandro et al.
Thirty-seventh Conference on Neural Information Processing Systems. 2023.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

do Carmo Alves, MA, Varma, A, Soriano Marcolino, L & Elkhatib, Y 2023, Information-guided Planning: An Online Approach for Partially Observable Problems. in Thirty-seventh Conference on Neural Information Processing Systems. Thirty-seventh Conference on Neural Information Processing Systems, New Orleans, Louisiana, United States, 10/12/23.

APA

do Carmo Alves, M. A., Varma, A., Soriano Marcolino, L., & Elkhatib, Y. (in press). Information-guided Planning: An Online Approach for Partially Observable Problems. In Thirty-seventh Conference on Neural Information Processing Systems

Vancouver

do Carmo Alves MA, Varma A, Soriano Marcolino L, Elkhatib Y. Information-guided Planning: An Online Approach for Partially Observable Problems. In Thirty-seventh Conference on Neural Information Processing Systems. 2023

Author

do Carmo Alves, Matheus Aparecido ; Varma, Amokh ; Soriano Marcolino, Leandro et al. / Information-guided Planning : An Online Approach for Partially Observable Problems. Thirty-seventh Conference on Neural Information Processing Systems. 2023.

Bibtex

@inproceedings{02a113013454423681447494ca8d9d16,
title = "Information-guided Planning: An Online Approach for Partially Observable Problems",
abstract = "This paper presents IB-POMCP, a novel algorithm for online planning under partial observability. Our approach enhances the decision-making process by using estimations of the world belief's entropy to guide a tree search process and surpass the limitations of planning in scenarios with sparse reward configurations. By performing what we denominate as an information-guided planning process, the algorithm, which incorporates a novel I-UCB function, shows significant improvements in reward and reasoning time compared to state-of-the-art baselines in several benchmark scenarios, along with theoretical convergence guarantees.",
keywords = "Information-guided planning, Planning under uncertainty, Sequential decision making",
author = "{do Carmo Alves}, {Matheus Aparecido} and Amokh Varma and {Soriano Marcolino}, Leandro and Yehia Elkhatib",
year = "2023",
month = sep,
day = "21",
language = "English",
booktitle = "Thirty-seventh Conference on Neural Information Processing Systems",
note = "Thirty-seventh Conference on Neural Information Processing Systems : 37th Anniversary Conference, NeurIPS 2023 ; Conference date: 10-12-2023 Through 16-12-2023",
url = "https://neurips.cc/",

}

RIS

TY - GEN

T1 - Information-guided Planning

T2 - Thirty-seventh Conference on Neural Information Processing Systems

AU - do Carmo Alves, Matheus Aparecido

AU - Varma, Amokh

AU - Soriano Marcolino, Leandro

AU - Elkhatib, Yehia

N1 - Conference code: 37

PY - 2023/9/21

Y1 - 2023/9/21

N2 - This paper presents IB-POMCP, a novel algorithm for online planning under partial observability. Our approach enhances the decision-making process by using estimations of the world belief's entropy to guide a tree search process and surpass the limitations of planning in scenarios with sparse reward configurations. By performing what we denominate as an information-guided planning process, the algorithm, which incorporates a novel I-UCB function, shows significant improvements in reward and reasoning time compared to state-of-the-art baselines in several benchmark scenarios, along with theoretical convergence guarantees.

AB - This paper presents IB-POMCP, a novel algorithm for online planning under partial observability. Our approach enhances the decision-making process by using estimations of the world belief's entropy to guide a tree search process and surpass the limitations of planning in scenarios with sparse reward configurations. By performing what we denominate as an information-guided planning process, the algorithm, which incorporates a novel I-UCB function, shows significant improvements in reward and reasoning time compared to state-of-the-art baselines in several benchmark scenarios, along with theoretical convergence guarantees.

KW - Information-guided planning

KW - Planning under uncertainty

KW - Sequential decision making

UR - https://github.com/lsmcolab/ib-pomcp/

M3 - Conference contribution/Paper

BT - Thirty-seventh Conference on Neural Information Processing Systems

Y2 - 10 December 2023 through 16 December 2023

ER -