How to Reduce Action Space for Planning Domains? (Student Abstract)

Harsha Kokel, Junkyu Lee, Michael Katz, Shirin Sohrabi, Kavitha Srinivas

AAAI 2022 pp. 12989-12990

doi:10.1609/AAAI.V36I11.21631 /aaai/2022/kokel2022aaai-reduce/

Abstract

While AI planning and Reinforcement Learning (RL) solve sequential decision-making problems, they are based on different formalisms, which leads to a significant difference in their action spaces. When solving planning problems using RL algorithms, we have observed that a naive translation of the planning action space incurs severe degradation in sample complexity. In practice, those action spaces are often engineered manually in a domain-specific manner. In this abstract, we present a method that reduces the parameters of operators in AI planning domains by introducing a parameter seed set problem and casting it as a classical planning task. Our experiment shows that our proposed method significantly reduces the number of actions in the RL environments originating from AI planning domains.

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Cite

Text

Kokel et al. "How to Reduce Action Space for Planning Domains? (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21631

Markdown

[Kokel et al. "How to Reduce Action Space for Planning Domains? (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/kokel2022aaai-reduce/) doi:10.1609/AAAI.V36I11.21631

BibTeX

@inproceedings{kokel2022aaai-reduce,
  title     = {{How to Reduce Action Space for Planning Domains? (Student Abstract)}},
  author    = {Kokel, Harsha and Lee, Junkyu and Katz, Michael and Sohrabi, Shirin and Srinivas, Kavitha},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12989-12990},
  doi       = {10.1609/AAAI.V36I11.21631},
  url       = {https://mlanthology.org/aaai/2022/kokel2022aaai-reduce/}
}