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We examine the problem of the existence of optimal deterministic stationary strategiesintwo-players antagonistic (zero-sum) perfect information stochastic games with finitely many states and actions.We show that the existenceof such…

Computer Science and Game Theory · Computer Science 2016-11-28 Hugo Gimbert , Wieslaw Zielonka

Random selection, leader election, and collective coin flipping are fundamental tasks in fault-tolerant distributed computing. We study these problems in the full-information model where despite decades of study, key gaps remain in our…

Computational Complexity · Computer Science 2026-04-30 Eshan Chattopadhyay , Mohit Gurumukhani , Noam Ringach , Rocco A. Servedio

A frequently studied performance measure in online optimization is competitive analysis. It corresponds to the worst-case ratio, over all possible inputs of an algorithm, between the performance of the algorithm and the optimal offline…

Optimization and Control · Mathematics 2024-05-30 Antoine Lhomme , Nicolas Catusse , Nadia Brauner

Through a stochastic control theoretic approach, we analyze reputation games where a strategic long-lived player acts in a sequential repeated game against a collection of short-lived players. The key assumption in our model is that the…

Optimization and Control · Mathematics 2020-01-22 Nuh Aygün Dalkıran , Serdar Yüksel

We study the classical problem of prediction with expert advice in the adversarial setting with a geometric stopping time. In 1965, Cover gave the optimal algorithm for the case of 2 experts. In this paper, we design the optimal algorithm,…

Machine Learning · Computer Science 2016-07-12 Nick Gravin , Yuval Peres , Balasubramanian Sivan

Under the uncoupled learning setup, the last-iterate convergence guarantee towards Nash equilibrium is shown to be impossible in many games. This work studies the last-iterate convergence guarantee in general games toward rationalizability,…

Computer Science and Game Theory · Computer Science 2023-12-27 Jibang Wu , Haifeng Xu , Fan Yao

Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal…

Theoretical Economics · Economics 2020-03-24 Arthur Charpentier , Romuald Elie , Carl Remlinger

In this paper we introduce the novel framework of distributionally robust games. These are multi-player games where each player models the state of nature using a worst-case distribution, also called adversarial distribution. Thus each…

Optimization and Control · Mathematics 2017-07-25 Dario Bauso , Jian Gao , Hamidou Tembine

We consider autonomous racing of two cars and present an approach to formulate racing decisions as a non-cooperative non-zero-sum game. We design three different games where the players aim to fulfill static track constraints as well as…

Optimization and Control · Mathematics 2019-01-11 Alexander Liniger , John Lygeros

We show, under weaker assumptions than in the previous literature, that a perpetual optimal stopping game always has a value. We also show that there exists an optimal stopping time for the seller, but not necessarily for the buyer.…

Probability · Mathematics 2016-08-16 Erik Ekström , Stephane Villeneuve

In this study, we consider the infinitely many-armed bandit problems in a rested rotting setting, where the mean reward of an arm may decrease with each pull, while otherwise, it remains unchanged. We explore two scenarios regarding the…

Machine Learning · Computer Science 2025-06-03 Jung-hun Kim , Milan Vojnovic , Se-Young Yun

We study two-player concurrent stochastic games on finite graphs, with B\"uchi and co-B\"uchi objectives. The goal of the first player is to maximize the probability of satisfying the given objective. Following Martin's determinacy theorem…

Computer Science and Game Theory · Computer Science 2022-11-28 Benjamin Bordais , Patricia Bouyer , Stéphane Le Roux

We consider a general class of round-robin tournament models of equally strong players. In these models, each of the $n$ players competes against every other player exactly once. For each match between two players, the outcome is a value…

Probability · Mathematics 2026-05-21 Yaakov Malinovsky

We introduce a new non-zero-sum game of optimal stopping with asymmetric exercise opportunities. Given a stochastic process modelling the value of an asset, one player observes and can act on the process continuously, while the other player…

Probability · Mathematics 2024-05-16 José Luis Pérez , Neofytos Rodosthenous , Kazutoshi Yamazaki

This paper aims to put forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an unbounded number of exploratory trials, provided that one…

Machine Learning · Computer Science 2021-04-01 Agustin Castellano , Juan Bazerque , Enrique Mallada

We consider a sequential decision-making problem where an agent can take one action at a time and each action has a stochastic temporal extent, i.e., a new action cannot be taken until the previous one is finished. Upon completion, the…

Machine Learning · Computer Science 2020-03-26 P Sharoff , Nishant A. Mehta , Ravi Ganti

Bidding chess is a chess variant where instead of alternating play, players bid for the opportunity to move. Generalizing a known result on so-called Richman games, we show that for a natural class of games including bidding chess, each…

Combinatorics · Mathematics 2017-03-07 Urban Larsson , Johan Wästlund

The beautiful theory of statistical gambling, started by Dubins and Savage (for subfair games) and continued by Kelly and Breiman (for superfair games) has mostly been studied under the unrealistic assumption that we live in a continuous…

Probability · Mathematics 2014-11-27 Shalosh B. Ekhad , Doron Zeilberger

We consider an autonomous navigation problem, whereby a traveler aims at traversing an environment in which an adversary tries to set an ambush. A two players zero sum game is introduced. Players' strategies are computed as random path…

Robotics · Computer Science 2016-12-08 Emmanuel Boidot , Aude Marzuoli , Eric Feron

Suppose a gambler starts with a fortune in (0,1) and wishes to attain a fortune of 1 by making a sequence of bets. Assume thay whenever the gambler stakes the amount s, the gambler's fortune increases by s with probability w and decreases…

Probability · Mathematics 2007-05-23 Jason Schweinsberg
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