Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Faster MIL-based Subgoal Identification for Reinforcement Learning by Tuning Fewer Hyperparameters
Download
index.pdf
Date
2024-4-20
Author
Sunel, Saim
Çilden, Erkin
Polat, Faruk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
27
views
11
downloads
Cite This
Various methods have been proposed in the literature for identifying subgoals in discrete reinforcement learning (RL) tasks. Once subgoals are discovered, task decomposition methods can be employed to improve the learning performance of agents. In this study, we classify prominent subgoal identification methods for discrete RL tasks in the literature into the following three categories: graph-based, statistics-based, and multi-instance learning (MIL)-based. As contributions, first, we introduce a new MIL-based subgoal identification algorithm called EMDD-RL and experimentally compare it with a previous MIL-based method. The previous approach adapts MIL's Diverse Density (DD) algorithm, whereas our method considers Expected-Maximization Diverse Density (EMDD). The advantage of EMDD over DD is that it can yield more accurate results with less computation demand thanks to the expectation-maximization algorithm. EMDD-RL modifies some of the algorithmic steps of EMDD to identify subgoals in discrete RL problems. Second, we evaluate the methods in several RL tasks for the hyperparameter tuning overhead they incur. Third, we propose a new RL problem called key-room and compare the methods for their subgoal identification performances in this new task. Experiment results show that MIL-based subgoal identification methods could be preferred to the algorithms of the other two categories in practice.
Subject Keywords
diverse density
,
expectation-maximization
,
hyperparameter search
,
multiple instance learning
,
reinforcement learning
,
Subgoal identification
URI
https://hdl.handle.net/11511/110345
Journal
ACM Transactions on Autonomous and Adaptive Systems
DOI
https://doi.org/10.1145/3643852
Collections
Department of Computer Engineering, Article
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. Sunel, E. Çilden, and F. Polat, “Faster MIL-based Subgoal Identification for Reinforcement Learning by Tuning Fewer Hyperparameters,”
ACM Transactions on Autonomous and Adaptive Systems
, vol. 19, no. 2, pp. 0–0, 2024, Accessed: 00, 2024. [Online]. Available: https://hdl.handle.net/11511/110345.