On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games

By Eric V. Mazumdar, Michael I. Jordan, S. Sastry. 2019

15 citations recorded by the source corpus.

Subfields: Reinforcement Learning, Game & Decision Theory

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Citations in the corpus

A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games (2019, background): “However, as shown by Adolphs et al. (2019); Mazumdar et al. (2019), stationary points are not necessarily Nash equilibria in general but if v is monotone, these two notions are equivalent.”
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks (2019, methodology): “In order to reach a local Nash equilibrium, Adolphs et al. (2018); Mazumdar et al. (2019) develop techniques based on second order information.”
Accelerating Smooth Games by Manipulating Spectral Shapes (2020, background): “, 2014; Pfau and Vinyals, 2016) have caused a surge of interest in the theoretical understanding of first-order methods for the solution of differentiable multi-player games (Palaniappan and Bach, 2016; Gidel et al., 2019a; Balduzzi et al., 2018; Mescheder et al., 2017, 2018; Mazumdar et al., 2019).”
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, methodology): “Consensus optimization [307], symplectic gradient adjustment [312], and extragradient method [310] have been advocated to mitigate the oscillatory behaviors around the equilibria; while [308, 305]”
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games (2019, unknown): “, 2014), the need to robustify learning systems with regard to against actual adversaries (Xu et al., 2009) and with regard to mismatch between assumptions and data-generating mechanisms (Yang, 2011; Giordano et al.”