3 citations · 3 across the 2 of their papers we have counts for
5 papers
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
Lucas Schott, Josephine Delas, Hatem Hajri +5
Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments. Despite its significant…
Stein Variational Black-Box Combinatorial Optimization
Thomas Landais, Olivier Goudet, Adrien Goëffon +2
Combinatorial black-box optimization in high-dimensional settings demands a careful trade-off between exploiting promising regions of the search space and preserving sufficient exp…
Reward-Preserving Attacks For Robust Reinforcement Learning
Lucas Schott, Elies Gherbi, Hatem Hajri +1
Adversarial training in reinforcement learning (RL) is challenging because perturbations cascade through trajectories and compound over time, making fixed-strength attacks either o…
Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learning
Olivier Goudet, Quentin Suire, Adrien Goëffon +2
We introduce an order-invariant reinforcement learning framework for black-box combinatorial optimization. Classical estimation-of-distribution algorithms (EDAs) often rely on lear…
Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
Jean Pinsolle, Olivier Goudet, Cyrille Enderli +2
In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exp…