9 citations · 12 across the 10 of their papers we have counts for
11 papers
The Game Changer Problem: Controlling Equilibria with Discrete Rewards
Brandon Han, Young Wu, Shiyun Cheng +1
We introduce the game changer problem, where an external designer modifies a game's reward matrix to make a target pure action profile the unique equilibrium, subject to the constr…
Pure Nash Equilibria under the Affine Mechanism: A Potential Game of Exaggeration
Jason Jisen Li, Young Wu, Yancheng Zhu +2
The mean mechanism is known to be non-incentive-compatible, namely, rational players are incentivized to misreport their values. Despite this game-theoretic issue, the mean mechani…
Optimally Installing Strict Equilibria
Jeremy McMahan, Young Wu, Yudong Chen +2
In this work, we develop a reward design framework for installing a desired behavior as a strict equilibrium across standard solution concepts: dominant strategy equilibrium, Nash…
The Battling Influencers Game: Nash Equilibria Structure of a Potential Game and Implications to Value Alignment
Young Wu, Yancheng Zhu, Jin-Yi Cai +1
When multiple influencers attempt to compete for a receiver's attention, their influencing strategies must account for the presence of one another. We introduce the Battling Influe…
Inception: Efficiently Computable Misinformation Attacks on Markov Games
Jeremy McMahan, Young Wu, Yudong Chen +2
We study security threats to Markov games due to information asymmetry and misinformation. We consider an attacker player who can spread misinformation about its reward function to…
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…
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning
Young Wu, Jeremy McMahan, Xiaojin Zhu +1
In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given dataset. We study reward-poisoning attacks in this setting where an exogenous attacker m…
Game Redesign in No-regret Game Playing
Yuzhe Ma, Young Wu, Xiaojin Zhu
We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the origin…