9 citations · 12 across the 10 of their papers we have counts for
4 papers · 1 filter
Optimal Attack and Defense for Reinforcement Learning
Jeremy McMahan, Young Wu, Xiaojin Zhu +1
To ensure the usefulness of Reinforcement Learning (RL) in real systems, it is crucial to ensure they are robust to noise and adversarial attacks. In adversarial RL, an external at…
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen +3
We study the game modification problem, where a benevolent game designer or a malevolent adversary modifies the reward function of a zero-sum Markov game so that a target determini…
VISER: A Tractable Solution Concept for Games with Information Asymmetry
Jeremy McMahan, Young Wu, Yudong Chen +2
Many real-world games suffer from information asymmetry: one player is only aware of their own payoffs while the other player has the full game information. Examples include the cr…
Data Poisoning to Fake a Nash Equilibrium in Markov Games
Young Wu, Jeremy McMahan, Xiaojin Zhu +1
We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning (MARL), where an attacker may change a data set in an attempt to install a (potentially fictiti…