Reward-Free Attacks in Multi-Agent Reinforcement Learning
Reward-Free Attacks in Multi-Agent Reinforcement Learning
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 where the attacker wants to behave strategically when the victim's motivations are unknown. We argue that one heuristic approach …