SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent
Reinforcement Learning Systems

SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems

6 February 2024

Yuwen Pu

Papers citing "SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems"

5 / 5 papers shown

Title
UNIDOOR: A Universal Framework for Action-Level Backdoor Attacks in Deep Reinforcement Learning Oubo Ma L. Du Yang Dai Chunyi Zhou Qingming Li Yuwen Pu Shouling Ji 46 0 0 28 Jan 2025
Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL Xiangyu Liu Souradip Chakraborty Yanchao Sun Furong Huang AAML 26 4 0 27 May 2023
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability Dibya Ghosh Jad Rahme Aviral Kumar Amy Zhang Ryan P. Adams Sergey Levine OffRL 272 109 0 13 Jul 2021
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning Lun Wang Zaynah Javed Xian Wu Wenbo Guo Xinyu Xing D. Song AAML 163 64 0 02 May 2021
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 340 1,955 0 04 May 2020