Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics

Understand

Poisoning attacks on Reinforcement Learning (RL) systems could take advantage of RL algorithm's vulnerabilities and cause failure of the learning.

• However, prior works on poisoning RL usually either unrealistically assume the attacker knows the underlying Markov Decision Process (MDP), or directly apply the poisoning methods in supervised learning to RL.
• In this work, we build a generic poisoning framework for online RL via a comprehensive investigation of heterogeneous poisoning models in RL.
• Without any prior knowledge of the MDP, we propose a strategic poisoning algorithm called Vulnerability-Aware Adversarial Critic Poison (VA2C-P), which works for most policy-based deep RL agents, closing the gap that no poisoning method exists for policy-based RL agents.