no code implementations • ICML 2020 • Tanner Fiez, Benjamin Chasnov, Lillian Ratliff
Contemporary work on learning in continuous games has commonly overlooked the hierarchical decision-making structure present in machine learning problems formulated as games, instead treating them as simultaneous play games and adopting the Nash equilibrium solution concept.
no code implementations • 2 Feb 2023 • Chinmay Maheshwari, James Cheng, S. Shankar Sasty, Lillian Ratliff, Eric Mazumdar
In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner.
no code implementations • 18 Jul 2022 • Georgios Piliouras, Lillian Ratliff, Ryann Sim, Stratis Skoulakis
The study of learning in games has thus far focused primarily on normal form games.
no code implementations • 5 Jul 2022 • Zhaoqi Li, Lillian Ratliff, Houssam Nassif, Kevin Jamieson, Lalit Jain
In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied.
no code implementations • NeurIPS 2021 • Tanner Fiez, Lillian Ratliff, Eric Mazumdar, Evan Faulkner, Adhyyan Narang
For the class of nonconvex-PL zero-sum games, we exploit timescale separation to construct a potential function that when combined with the stability characterization and an asymptotic saddle avoidance result gives a global asymptotic almost-sure convergence guarantee to a set of the strict local minmax equilibrium.
no code implementations • NeurIPS 2021 • Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian Ratliff
Classical learning results build on this theorem to show that online no-regret dynamics converge to an equilibrium in a time-average sense in zero-sum games.
1 code implementation • 15 Dec 2020 • Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras, Lillian Ratliff
The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game.
1 code implementation • ICLR 2021 • Tanner Fiez, Lillian Ratliff
In this work, we bridge the gap between past work by showing there exists a finite timescale separation parameter $\tau^{\ast}$ such that $x^{\ast}$ is a stable critical point of gradient descent-ascent for all $\tau \in (\tau^{\ast}, \infty)$ if and only if it is a strict local minmax equilibrium.
1 code implementation • 27 Jun 2020 • Tanner Fiez, Nihar B. Shah, Lillian Ratliff
Theoretically, we show a local optimality guarantee of our algorithm and prove that popular baselines are considerably suboptimal.
no code implementations • L4DC 2020 • Liyuan Zheng, Lillian Ratliff
Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints.
1 code implementation • NeurIPS 2019 • Tanner Fiez, Lalit Jain, Kevin Jamieson, Lillian Ratliff
Such a transductive setting naturally arises when the set of measurement vectors is limited due to factors such as availability or cost.