3 citations · 6 across the 7 of their papers we have counts for
8 papers
Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design
Andreas Schlaginhaufen, Reda Ouhamma, Maryam Kamgarpour
We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in th…
Nash equilibria in scalar discrete-time linear quadratic games
Giulio Salizzoni, Reda Ouhamma, Maryam Kamgarpour
An open problem in linear quadratic (LQ) games has been characterizing the Nash equilibria. This problem has renewed relevance given the surge of work on understanding the converge…
Finite-time convergence to an -efficient Nash equilibrium in potential games
Anna Maddux, Reda Ouhamma, Maryam Kamgarpour
This paper investigates the convergence time of log-linear learning to an -efficient Nash equilibrium in potential games, where an efficient Nash equilibrium is defined as the m…
Learning in Zero-Sum Markov Games: Relaxing Strong Reachability and Mixing Time Assumptions
Reda Ouhamma, Maryam Kamgarpour
We address payoff-based decentralized learning in infinite-horizon zero-sum Markov games. In this setting, each player makes decisions based solely on received rewards, without obs…
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning
Reda Ouhamma, Debabrota Basu, Odalric-Ambrym Maillard
We study the problem of episodic reinforcement learning in continuous state-action spaces with unknown rewards and transitions. Specifically, we consider the setting where the rewa…
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge
Reda Ouhamma, Odalric Maillard, Vianney Perchet
We consider the problem of online linear regression in the stochastic setting. We derive high probability regret bounds for online ridge regression and the forward algorithm. This…
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
Reda Ouhamma, Rémy Degenne, Pierre Gaillard +1
In the fixed budget thresholding bandit problem, an algorithm sequentially allocates a budgeted number of samples to different distributions. It then predicts whether the mean of e…
Learning Value Functions in Deep Policy Gradients using Residual Variance
Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard +1
Policy gradient algorithms have proven to be successful in diverse decision making and control tasks. However, these methods suffer from high sample complexity and instability issu…