Search Results for author: Emmanuel Rachelson

Found 9 papers, 3 papers with code

Disentangled cyclic reconstruction for domain adaptation

no code implementations1 Jan 2021 David Bertoin, Emmanuel Rachelson

The domain adaptation problem involves learning a unique classification or regres-sion model capable of performing on both a source and a target domain.

Unsupervised Domain Adaptation

Lipschitz Lifelong Reinforcement Learning

1 code implementation15 Jan 2020 Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman

We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks.

Transfer Learning

Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location

no code implementations12 Jul 2019 Andrea Lodi, Luca Mossina, Emmanuel Rachelson

Although presented through the application to the facility location problem, the approach developed here is general and explores a new perspective on the exploitation of past experience in combinatorial optimization.

Combinatorial Optimization

Open Loop Execution of Tree-Search Algorithms, extended version

no code implementations3 May 2018 Erwan Lecarpentier, Guillaume Infantes, Charles Lesire, Emmanuel Rachelson

In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action.

Naive Bayes Classification for Subset Selection

1 code implementation19 Jul 2017 Luca Mossina, Emmanuel Rachelson

This article focuses on the question of learning how to automatically select a subset of items among a bigger set.

Classification General Classification +1

Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring

no code implementations18 Jul 2017 Erwan Lecarpentier, Sebastian Rapp, Marc Melo, Emmanuel Rachelson

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.

Q-Learning

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