1 citations · 1 across the 10 of their papers we have counts for
11 papers
Ex-Post Equilibria: Structure and Computation
Francesco Giordano, Julien Grand-Clément, Christian Kroer
This paper studies ex-post equilibria (EPEs) in simultaneous-move games with parameter uncertainty. We first compare EPEs with existing notions of robust equilibrium and provide a…
Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes
Axel Benyamine, Julien Grand-Clément, Marek Petrik +2
In this paper, we propose a general theory of ambiguity-averse MDPs, which treats the uncertain transition probabilities as random variables and evaluates a policy via a risk measu…
Thresholds for sensitive optimality and Blackwell optimality in stochastic games
Stéphane Gaubert, Julien Grand-Clément, Ricardo D. Katz
We investigate refinements of the mean-payoff criterion in two-player zero-sum perfect-information stochastic games. A strategy is Blackwell optimal if it is optimal in the discoun…
Playing against a stationary opponent
Julien Grand-Clément, Nicolas Vieille
This paper investigates properties of Blackwell -optimal strategies in zero-sum stochastic games when the adversary is restricted to stationary strategies, motivated by applicat…
On Separation Between Best-Iterate, Random-Iterate, and Last-Iterate Convergence of Learning in Games
Yang Cai, Gabriele Farina, Julien Grand-Clément +4
Non-ergodic convergence of learning dynamics in games is widely studied recently because of its importance in both theory and practice. Recent work (Cai et al., 2024) showed that a…
Dynamic Programming-Compatible Uncertainty Sets in Robust Markov Decision Processes
Julien Grand-Clément, Nian Si, Shengbo Wang
In this paper, we investigate the compatibility of robust Markov Decision Processes (RMDPs) with dynamic programming under various assumptions on the uncertainty set, i.e., we inve…
Risk-averse Total-reward MDPs with ERM and EVaR
Xihong Su, Julien Grand-Clément, Marek Petrik
Optimizing risk-averse objectives in discounted MDPs is challenging because most models do not admit direct dynamic programming equations and require complex history-dependent poli…
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms
Yang Cai, Gabriele Farina, Julien Grand-Clément +4
Self-play via online learning is one of the premier ways to solve large-scale two-player zero-sum games, both in theory and practice. Particularly popular algorithms include optimi…
Extensive-Form Game Solving via Blackwell Approachability on Treeplexes
Darshan Chakrabarti, Julien Grand-Clément, Christian Kroer
In this paper, we introduce the first algorithmic framework for Blackwell approachability on the sequence-form polytope, the class of convex polytopes capturing the strategies of p…
Beyond discounted returns: Robust Markov decision processes with average and Blackwell optimality
Julien Grand-Clément, Marek Petrik, Nicolas Vieille
Robust Markov Decision Processes (RMDPs) are a widely used framework for sequential decision-making under parameter uncertainty. RMDPs have been extensively studied when the object…
Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
Yang Cai, Gabriele Farina, Julien Grand-Clément +4
We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching (RM). Despite their widespread use for solving rea…