Exploració per autor "Pou Mulet, Bartomeu"
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Adaptive optics control with multi-agent model-free reinforcement learning
Pou Mulet, Bartomeu; Ferreira, Florian; Quiñones Moreno, Eduardo; Gratadour, Damien; Martín Muñoz, Mario (2022-01-14)
Article
Accés obertWe present a novel formulation of closed-loop adaptive optics (AO) control as a multi-agent reinforcement learning (MARL) problem in which the controller is able to learn a non-linear policy and does not need a priori ... -
Adaptive optics control with reinforcement learning: first steps
Pou Mulet, Bartomeu; Quiñones, Eduardo; Martín Muñoz, Mario (Barcelona Supercomputing Center, 2021-05)
Text en actes de congrés
Accés obertWhen planar wavefronts from distant stars traverse the atmosphere, they become distorted due to the atmosphere’s inhomogeneous temperature distribution. Adaptive Optics (AO) is the field in charge of correcting those ... -
Denoising wavefront sensor image with deep neural networks
Pou Mulet, Bartomeu; Quiñones Moreno, Eduardo; Gratadour, Damien; Martín Muñoz, Mario (International Society for Photo-Optical Instrumentation Engineers (SPIE), 2020)
Text en actes de congrés
Accés obertA classical closed-loop adaptive optics system with a Shack-Hartmann wavefront sensor (WFS) relies on a center of gravity approach to process the WFS information and an integrator with gain to produce the commands to a ... -
Model-free reinforcement learning with a non-linear reconstructor for closed-loop adaptive optics control with a pyramid wavefront sensor
Pou Mulet, Bartomeu; Smith, Jeffrey; Quiñones Moreno, Eduardo; Martín Muñoz, Mario; Gratadour, Damien (International Society for Photo-Optical Instrumentation Engineers (SPIE), 2022)
Text en actes de congrés
Accés obertWe present a model-free reinforcement learning (RL) predictive model with a supervised learning non-linear reconstructor for adaptive optics (AO) control with a pyramid wavefront sensor (P-WFS). First, we analyse the ... -
Speeding up Reinforcement Learning with Learned Models
Pou Mulet, Bartomeu (Universitat Politècnica de Catalunya, 2019-10-16)
Projecte Final de Màster Oficial
Accés obertIn this master thesis, we have tried to solve two of most prominent Reinforcement Learning problems: sparse rewards and sample efficiency. The combination of Model Based Reinforcement Learning, Hindsight Experience Replay ...