Reward-Free Attacks in Multi-Agent Reinforcement Learning

Ted Fujimoto, Timothy Doster, Adam Attarian +2

We investigate how effective an attacker can be when it only learns from its victim's actions, without access to the victim's reward. In this work, we are motivated by the scenario…