Baffle : Hiding backdoors in offline reinforcement learning datasets

• Main Authors: GONG, Chen, YANG, Zhou, BAI, Yunpeng, HE, Junda, SHI, Jieke, LI, Kecen, SINHA, Arunesh, XU, Bowen, HOU, Xinwen, David LO, WANG, Tianhao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Offline reinforcement learning; Backdoor attack; Dataset security threats; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9887; https://ink.library.smu.edu.sg/context/sis_research/article/10887/viewcontent/2210.04688v5.pdf
• Institution: Singapore Management University

Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected d...