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- Emmanuel Rachelson, Michail G. Lagoudakis
- ISAIM
- 2010

In the field of sequential decision making and reinforcement learning, it has been observed that good policies for most problems exhibit a significant amount of structure. In practice, this implies that when a learning agent discovers an action is better than any other in a given state, this action actually happens to also dominate in a certain… (More)

- Nicolas Regis, Frédéric Dehais, +4 authors Catherine Tessier
- IEEE Trans. Human-Machine Systems
- 2014

The allocation of visual attention is a key factor for the humans when operating complex systems under time pressure with multiple information sources. In some situations, attentional tunneling is likely to appear and leads to excessive focus and poor decision making. In this study, we propose a formal approach to detect the occurrence of such an… (More)

- Emmanuel Rachelson, François Schnitzler, Louis Wehenkel, Damien Ernst
- ICAART
- 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 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… (More)

- Emmanuel Rachelson, Frédérick Garcia, Patrick Fabiani
- ISAIM
- 2008

Recent work on Markov Decision Processes (MDPs) covers the use of continuous variables and resources, including time. This work is usually done in a framework of bounded resources and finite temporal horizon for which a total reward criterion is often appropriate. However, most of this work considers discrete effects on continuous variables while… (More)

- Emmanuel Rachelson, Ala Ben Abbes, Sebastien Diemer
- 2010 22nd IEEE International Conference on Tools…
- 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 Learning method for the offline construction of a predictor which takes the problem's parameters as input and infers values for the discrete optimization… (More)

- Emmanuel Rachelson, Patrick Fabiani, Frédérick Garcia
- 2009 21st IEEE International Conference on Tools…
- 2009

We introduce TiMDPpoly , an algorithm designed to solve planning problems with durative actions, under probabilistic uncertainty, in a non-stationary, continuous-time context. Mission planning for autonomous agents such as planetary rovers or unmanned aircrafts often correspond to such time-dependent planning problems. Modeling these problems can be cast… (More)

Résumé : Time is a crucial variable in planning and often requires special attention since it introduces a specific structure along with additional complexity, especially in the case of decision under uncertainty. In this paper, after reviewing and comparing MDP frameworks designed to deal with temporal problems, we focus on Generalized Semi-Markov Decision… (More)

- Ankit Chiplunkar, Emmanuel Rachelson, Michele Colombo, Joseph Morlier
- ICPRAM
- 2016

- Erwan Lecarpentier, Sebastian Rapp, Marc Melo, Emmanuel Rachelson
- ArXiv
- 2017

Autonomous unpowered flight is a challenge for control and guidance systems: all the energy the aircraft might use during flight has to be harvested directly from the atmosphere. We investigate the design of an algorithm that optimizes the closed-loop control of a glider’s bank and sideslip angles, while flying in the lower convective layer of the… (More)

- Luca Mossina, Emmanuel Rachelson
- ArXiv
- 2017

This article focuses on the question of learning how to automatically select a subset of items among a bigger set. We introduce a methodology for the inference of ensembles of discrete values, based on the Naive Bayes assumption. Our motivation stems from practical use cases where one wishes to predict an unordered set of (possibly interdependent) values… (More)