Work

Discovering Options That Minimize Average Planning Time

Year: 2025

Type: conference-paper

Abstract: We present an option discovery algorithm that accelerates planning by minimizing the shortest distance between any two states in the MDP. The proposed algorithm produces options that approximately minimize planning time in the multi-goal setting: it is shown to be a worst case (4-alpha, 2)-approximation of the optimal option set, where alpha is the approximation ratio of the k-medians with penalties subroutine. We then present a variation, "Fast Average Options", with improved run-time and describe a general means of producing similar algorithms based on selection of a k-medians subroutine. We empirically evaluate our method on four discrete and two continuous control planning domains and show that it outperforms other leading option discovery algorithms.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v39i17.33932

Authors: Alexander Ivanov, Akhil Bagaria, George Konidaris

Institutions: Brown University, Amazon (United States)

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Office of Naval Research

Awards: G4720003262, RI: Medium: Learning Task-Specific Representations for Broadly Capable Reinforcement Learning Agents, G8876996369