References of "Rachelson, Emmanuel"
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See detailOptimal sample selection for batch-mode reinforcement learning
Rachelson, Emmanuel ULg; Schnitzler, François ULg; Wehenkel, Louis ULg et al

in Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011) (2011)

We introduce the Optimal Sample Selection (OSS) meta-algorithm for solving discrete-time Optimal Control problems. This meta-algorithm maps the problem of finding a near-optimal closed-loop policy to the ... [more ▼]

We introduce the Optimal Sample Selection (OSS) meta-algorithm for solving discrete-time Optimal Control problems. This meta-algorithm maps the problem of finding a near-optimal closed-loop policy to the identification of a small set of one-step system transitions, leading to high-quality policies when used as input of a batch-mode Reinforcement Learning (RL) algorithm. We detail a particular instance of this OSS metaalgorithm that uses tree-based Fitted Q-Iteration as a batch-mode RL algorithm and Cross Entropy search as a method for navigating efficiently in the space of sample sets. The results show that this particular instance of OSS algorithms is able to identify rapidly small sample sets leading to high-quality policies [less ▲]

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See detailL’apprentissage au secours de la réduction de dimension pour des problèmes d’optimisation
Ben Abbes, Ala; Rachelson, Emmanuel ULg; Diemer, Sébastien

in Actes de la Conférence Francophone d'Apprentissage (2010, May 19)

Pour assurer la stabilité du réseau électrique, la production doit être ajustée en quasi temps réel à la consommation. Cet ajustement ne peut porter que sur un nombre limité de centrales et doit être ... [more ▼]

Pour assurer la stabilité du réseau électrique, la production doit être ajustée en quasi temps réel à la consommation. Cet ajustement ne peut porter que sur un nombre limité de centrales et doit être effectué dans des délais réduits. La combinatoire du problème rend la recherche d’un optimum économique par des méthodes d’optimisation classiques très difficile. Ce travail cherche à montrer l’intérêt d’utiliser des algorithmes d’apprentissage supervisé performants comme le Boosting, pour sélectionner les centrales à redéclarer. Cette sélection préalable permet ensuite de réduire considérablement le temps de l’optimisation des programmes de production tout en garantissant l’optimalité économique. [less ▲]

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See detailCombining Mixed Integer Programming and Supervised Learning for Fast Re-planning
Rachelson, Emmanuel ULg; Ben Abbes, Ala; Diemer, Sébastien

in Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence (2010)

We introduce a new plan repair method for problems cast as Mixed Integer Programs. In order to tackle the inherent complexity of these NP-hard problems, our approach relies on the use of Supervised ... [more ▼]

We introduce a new plan repair method for problems cast as Mixed Integer Programs. In order to tackle the inherent complexity of these NP-hard problems, our approach relies on the use of Supervised Learning method for the offline construction of a predictor which takes the problem’s parameters as input and infers values for the discrete optimization variables. This way, the online resolution time of the plan repair problem can be greatly decreased by avoiding a large part of the combinatorial search among discrete variables. This contribution was motivated by the large-scale problem of intra-daily recourse strategy computation in electrical power systems. We report and discuss results on this benchmark, illustrating the different aspects and mechanisms of this new approach which provided close-to-optimal solutions in only a fraction of the computational time necessary for existing solvers. [less ▲]

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