5 papers
Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
Sunwoo Lee, Mingu Kang, Yonghyeon Jo +1
Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter-agent interactions. Prior rob…
Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning
Yonghyeon Jo, Sunwoo Lee, Seungyul Han
Value decomposition is a core approach for cooperative multi-agent reinforcement learning (MARL). However, existing methods still rely on a single optimal action and struggle to ad…
Shaping Zero-Shot Coordination via State Blocking
Mingu Kang, Sunwoo Lee, Yonghyeon Jo +1
Zero-shot coordination (ZSC) aims to enable agents to cooperate with independently trained partners without prior interaction, a key requirement for real-world multi-agent systems…
Focusing Influence Mechanism for Multi-Agent Reinforcement Learning
Yisak Park, Sunwoo Lee, Seungyul Han
Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrate their influence, leading to in…
Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Sunwoo Lee, Jaebak Hwang, Yonghyeon Jo +1
Traditional robust methods in multi-agent reinforcement learning (MARL) often struggle against coordinated adversarial attacks in cooperative scenarios. To address this limitation,…