Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. (arXiv:2206.01888v1 [cs.LG])

We expose the danger of reward poisoning in offline multi-agent reinforcement
learning (MARL), whereby an attacker can modify the reward vectors to different
learners in an offline data set while incurring a poisoning cost. Based on the
poisoned data set, all rational learners using some confidence-bound-based MARL
algorithm will infer that a target policy - chosen by the attacker and not
necessarily a solution concept originally - is the Markov perfect dominant
strategy equilibrium for the underlying Markov Game, hence …

agent attacks lg poisoning