Feb 6, 2024 · In this study, we unveil, for the first time, the capability of attackers to generate adversarial policies even when restricted to partial observations of the ...
Jun 26, 2024 · We propose a novel black-box attack (SUB-PLAY) that incorporates the concept of constructing multiple subgames to mitigate the impact of partial observability.
Jul 5, 2024 · This paper is included in the Proceedings of the 30th USENIX Security Symposium. Xian Wu, Wenbo Guo, Hua Wei, Xinyu Xing.
Jun 26, 2024 · In this study, we unveil, for the first time, the capability of attackers togenerate adversarial policies even when restricted to partial ...
Jun 17, 2024 · This study proposes a novel black-box attack called SUB-PLAY that allows attackers to generate adversarial policies even when they have limited information ...
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems · 1 code implementation • 6 Feb 2024 • Oubo Ma, Yuwen Pu ...
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems · Oubo MaYuwen Pu +5 authors. Shouling Ji. Computer Science ...
Extensive evaluations demonstrate the effectiveness of SUB-PLAY under three typical partial observability limitations. Visualization ...
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Apr 14, 2024 · The paper proposes a new framework called the State-Adversarial Markov Game (SAMG) to model state uncertainties in Multi-Agent Reinforcement Learning (MARL) ...
The attacker's goal is to force the agents to learn a target policy or to maximize the cumulative rewards under some specific reward function chosen by the ...