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Publications associées (162)

Model-Free Iterative Learning Control With Nonrepetitive Trajectories for Second-Order MIMO Nonlinear Systems-Application to a Delta Robot

A model-free iterative learning control strategy (ILC) in nonrepetitive trajectories, applied to robotic manipulators is presented in this article. The development of this ILC controller is derived in general case for second-order nonlinear multi-input mul ...

2021

Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms

Fernando Porté Agel, Nicolas Otto Kirchner Bossi

In recent years, wind farm layout optimization (WFLO) has been extendedly developed to address the minimization of turbine wake effects in a wind farm. Considering that increasing the degrees of freedom in the decision space can lead to more efficient solu ...

MDPI2021

Reinforced Attention for Few-Shot Learning and Beyond

Tong Zhang

Few-shot learning aims to correctly recognize query samples from unseen classes given a limited number of support samples, often by relying on global embeddings of images. In this paper, we propose to equip the backbone network with an attention agent, whi ...

IEEE COMPUTER SOC2021

Modelling and process optimization for biodiesel production from Nannochloropsis salina using artificial neural network

Edgard Gnansounou

In the present investigation, calcium oxide solid nanocatalyst derived from the egg shell and Nannochloropsis salina were used for the production of biodiesel. The morphological characteristics and functional groups of synthesized nanocatalyst was characte ...

ELSEVIER SCI LTD2021

Improving Multi-agent Coordination by Learning to Estimate Contention

Boi Faltings, Panayiotis Danassis, Florian Wiedemair

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need ...

2021

Improving Multi-agent Coordination by Learning to Estimate Contention

Boi Faltings, Panayiotis Danassis, Florian Wiedemair

2021

Active Learning of Bayesian Probabilistic Movement Primitives

Jean-Marc Odobez, Sylvain Calinon, Hakan Girgin, Thibaut Antoine Kulak

Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distribu ...

IEEE2021

A Computational Framework For Two-Dimensional Random Walks With Restarts

Stefano Massei

The treatment of two-dimensional random walks in the quarter plane leads to Markov processes which involve semi-infinite matrices having Toeplitz or block Toeplitz structure plus a low-rank correction. We propose an extension of the framework introduced in ...

SIAM PUBLICATIONS2020

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Yu-Ting Huang, Kamalaruban Parameswaran, Cheng Shi

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

2020

Estimating Mean Bedload Transport Rates and Their Uncertainty

Christophe Ancey, Ivan Pascal

Measuring bedload transport rates usually involves measuring the flux of sediment or collecting sediment during a certain interval of time Δt. Because bedload transport rates exhibit significant non‐Gaussian fluctuations, their time‐averaged rates depend a ...

2020

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