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Learning to Play Sequential Games versus Unknown Opponents

Publications associées (51)

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Imitation Learning in Discounted Linear MDPs without exploration assumptions

Volkan Cevher, Efstratios Panteleimon Skoulakis, Luca Viano

We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs to sample from the environment. In particular, we re- move exploration assu ...

2024

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Two prominent categories for achieving coordinated multirobot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. While typic ...

2022

Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update

Efstratios Panteleimon Skoulakis

In this paper we provide a novel and simple algorithm, Clairvoyant Multiplicative Weights Updates (CMWU), for convergence to \textit{Coarse Correlated Equilibria} (CCE) in general games. CMWU effectively corresponds to the standard MWU algorithm but where ...

2022

Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds

Adam Teodor Polak, Marek Elias

We study the online problem of minimizing power consumption in systems with multiple power-saving states. During idle periods of unknown lengths, an algorithm has to choose between power-saving states of different energy consumption and wake-up costs. We d ...

2021

Mixed Strategies for Robust Optimization of Unknown Objectives

Maryam Kamgarpour, Andreas Krause, Ilija Bogunovic

We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter. For this setting, we design a novel sample-efficient algorithm GP-MRO, which sequentially le ...

PMLR2020

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

EPFL2019

A disaggregate choice-based approach to find epsilon-equilibria of oligopolistic markets

Michel Bierlaire, Virginie Janine Camille Lurkin, Stefano Bortolomiol

We present a general framework to find epsilon-equilibrium solutions of oligopolistic markets in which demand is modeled at the disaggregate level using discrete choice models. Consumer choices are modeled according to random utility theory, and the choice ...

2019

An Algorithm for Odor Source Localization based on Source Term Estimation

Alcherio Martinoli, Ali Marjovi, Faezeh Rahbar

Finding sources of airborne chemicals with mobile sensing systems finds applications across the security, safety, domestic, medical, and environmental domains. In this paper, we present an algorithm based on source term estimation for odor source localizat ...

2019

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IEEE2019

Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question: How can we design efficient algorithms for large-scale computation? In this thesis, we focus on devising a general strategy to addr ...

EPFL2018