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Reinforcement Learning in Factored Action Spaces using Tensor Decompositions

Mahajan, Anuj and Samvelyan, Mikayel and Mao, Lei and Makoviychuk, Viktor and Garg, Animesh and Kossaifi, Jean and Whiteson, Shimon and Zhu, Yuke and Anandkumar, Animashree (2021) Reinforcement Learning in Factored Action Spaces using Tensor Decompositions. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220714-224657047

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Abstract

We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factored action spaces using tensor decompositions. The goal of this abstract is twofold: (1) To garner greater interest amongst the tensor research community for creating methods and analysis for approximate RL, (2) To elucidate the generalised setting of factored action spaces where tensor decompositions can be used. We use cooperative multi-agent reinforcement learning scenario as the exemplary setting where the action space is naturally factored across agents and learning becomes intractable without resorting to approximation on the underlying hypothesis space for candidate solutions.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.48550/arXiv.2110.14538arXivDiscussion Paper
https://tensorworkshop.github.io/NeurIPS2021/index.htmlOrganizationConference home page
ORCID:
AuthorORCID
Garg, Animesh0000-0003-0482-4296
Kossaifi, Jean0000-0002-4445-3429
Zhu, Yuke0000-0002-9198-2227
Anandkumar, Animashree0000-0002-6974-6797
Additional Information:Attribution 4.0 International (CC BY 4.0)
Record Number:CaltechAUTHORS:20220714-224657047
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220714-224657047
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115607
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:15 Jul 2022 23:14
Last Modified:15 Jul 2022 23:14

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