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Jaebak Hwang

dblp:305/1487 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Reinforcement learning · 87% Trustworthy machine learning · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
multi-agent reinforcement learning		 0.912025
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning · ICML 2025
Machine learning › Reinforcement learning › multi-agent reinforcement learning
robust multi-agent reinforcement learning		 0.912025
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning · ICML 2025
Machine learning › Trustworthy machine learning
robustness		 0.312025
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning · ICML 2025

Methods — techniques the papers use, named apart from their topics

adversarial training · 0.9adversarial attack · 0.9
YearPublicationVenuePosition
2025 Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
abstract
Traditional robust methods in multi-agent reinforcement learning (MARL) often struggle against coordinated adversarial attacks in cooperative scenarios. To address this limitation, we propose the Wolfpack Adversarial Attack framework, inspired by wolf hunting strategies, which targets an initial agent and its assisting agents to disrupt cooperation. Additionally, we introduce the Wolfpack-Adversarial Learning for MARL (WALL) framework, which trains robust MARL policies to defend against the proposed Wolfpack attack by fostering system-wide collaboration. Experimental results underscore the devastating impact of the Wolfpack attack and the significant robustness improvements achieved by WALL. Our code is available at https://github.com/sunwoolee0504/WALL.
Sunwoo Lee 0006, Jaebak Hwang, Yonghyeon Jo, Seungyul Han
 ICML2