9 citations · 11 across the 8 of their papers we have counts for
9 papers
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…
Coupling-based Convergence Diagnostic and Stepsize Scheme for Stochastic Gradient Descent
Xiang Li, Qiaomin Xie
The convergence behavior of Stochastic Gradient Descent (SGD) crucially depends on the stepsize configuration. When using a constant stepsize, the SGD iterates form a Markov chain,…
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…
Roping in Uncertainty: Robustness and Regularization in Markov Games
Jeremy McMahan, Giovanni Artiglio, Qiaomin Xie
We study robust Markov games (RMG) with -rectangular uncertainty. We show a general equivalence between computing a robust Nash equilibrium (RNE) of a -rectangular RMG and co…
Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits
Yige Hong, Qiaomin Xie, Yudong Chen +1
We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subs…
Effectiveness of Constant Stepsize in Markovian LSA and Statistical Inference
Dongyan Huo, Yudong Chen, Qiaomin Xie
In this paper, we study the effectiveness of using a constant stepsize in statistical inference via linear stochastic approximation (LSA) algorithms with Markovian data. After esta…
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…
Reinforcement Learning for SBM Graphon Games with Re-Sampling
Peihan Huo, Oscar Peralta, Junyu Guo +2
The Mean-Field approximation is a tractable approach for studying large population dynamics. However, its assumption on homogeneity and universal connections among all agents limit…