agenda
1 nov. 2018 Vertigo Replay. 18h30. Mais qu'avez-vous fait à Solange ? ... Microcosmos : le peuple de l'herbe de Claude Nuridsany. & Marie Pérennou.
TITLE OF [THESIS DISSERTATION]
Examples include: Microcosmos Atlantis
de Raoul PECK (6x52 mn)
(Microcosmos Les Rivières pourpres
Deep Reinforcement Learning for Flow Control
From the movie “Microcosmos”. John McCarthy Experience Replay is critical to maximizing data efficiency avoids the destabilizing effects of learning ...
Into the Abyss: A Study of the mise en abyme
evidence for the 'replay of a replay' in the play. 4.1 Form in 'The Mousetrap'. Hamlet has an unusual form. The characters are a good guide to the structure
RUE DES CORDIERS 8
12 nov. 2012 50 MICROCOSMOS. 51 CORLLANDES ... 50 MICROCOSMOS. 52 BAN-HAU ... REPLAY. Bureau d'architecte. KUNZ-DUAL Workshop architectes associés.
Worlds Without Us: Some Types of Disanthropy
"Le Quotidien Des Insectes": Microcosmos films Microcosmos is massively elliptical
Zeitschrift für klassische Philologie © Franz Steiner Verlag Stuttga
Regarding the microcosmos of the house more particularly Nevett's recent study shown
COUPE DAFRIQUE DES NATIONS TOTAL ÉGYPTE 2019
3 juin 2019 nous confient regarder en replay sur my les essais libres du vendredi après le GP parce qu'ils trouvent le ton plus léger et drôle.
Homo Videoludens 2.0: de Pacman a la gamification
establece un microcosmos separado del resto de la existencia cotidiana de repetición (replay) un fenómeno nacido gracias a la aplicación de la.
microcosmos replay - PDFprof
[PDF] Deep Reinforcement Learning for Flow Control From the movie “Microcosmos” John McCarthy Experience Replay is critical to maximizing data efficiency
Microcosmos le peuple de lherbe en Streaming & Replay - Molotovtv
Microcosmos le peuple de l'herbe sur Ciné+ Famiz: Regardez ce programme sur Molotov l'app gratuite pour regarder la TV en direct et en replay
Microcosmos: el hombre del Nuevo Mundo y la tradición grecolatina
El artículo realiza un análisis de cómo apreciaron cosmógrafos médicos y frailes la naturaleza del hombre (indio peninsular inmigrado y criollo) del Nuevo
[PDF] A Subatomic Replica of Our Solar System Macrocosmos - viXraorg
Microcosmos was reflected in the Macrocosmos In addition to working on his theory of gravitation it is
Micro Cosmos by Thistroy Games - Kickstarter
10 mai 2022 · MICRO COSMOS is a 1-4 player card based resource management game in which players try to terraform planets create colonies and bring back
Divertissement - REPLAY Orange
Tous vos programmes TV : films et séries dans notre catégorie en replay et streaming Microcosmos le peuple de l'herbe PDF dans ARTE en scène
Ménigoute : la révolution du documentaire animalier
28 oct 2016 · "Océan" de Jacques Perrin "Microcosmos" de Claude Nuridsany et Marie Pérennou ou encore "L'ours"de Jean-Jacques Annaud ont été vus par des
Dossiers pédagogiques - Microcosmos - Les Grignoux
Ce dossier s'adresse d'abord aux enseignants du primaire et aux animateurs qui verront le film Microcosmos avec un jeune public (entre neuf et douze ans
Petros Koumoutsakos
ETH Zürich
Deep Reinforcement Learning for Flow Control
with: M. Gazzola (UIUC), A. Tchieu (Space-X), W. van Rees (MIT), S. Verma (FAU), D. Alexeev (NVIDIA)G. Novati, P. Vlachas, P. Weber
Artificial Intelligence:
Computational ability to achieve goals
Intelligence ?
From the movie "Microcosmos"
John McCarthy
Learning
toOptimize
STOCHASTIC OPTIMIZATION for FLUID MECHANICS
Black Box scenario
!In Fluid Mechanics gradients are not available or not useful !Commercial(Black box) solvers, Experimental Set-ups !Multiple Local Minima, Noisy Output DATASurrogates and Covariance Matrix Adaptation ES
Model to render search
more efficient than random sampling select evaluate parents x k k=1 offspring x k 2k=1 sample adaptP(x|⇡)
P meta-models to replace expensive evaluations in the selection process D. B ueche, N. Schrau dolp h, P. Koumoutsakos, Acceler atin g evolutionary algorithms with Gaussian process fitness function models,IEEE Trans. on Systems, Man and Cybernetics
,, 35,, 2005 N. Hansen, S. D. Mller, and P. Koumoutsakos, ÒReducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES),⌘Evolutionary computation
, vol. 11, iss. 1, p. 1-18, 2003.C-start is an escape pattern
Is C-start
optimal?Liao Lab's Channel - YouTube
Preparatory stroke
Propulsive stroke
Muller, van den Boogaart, van Leeuwen. J. of Exp.Biology, 2008.C-start is OUTCOME of optimization
1. Fluid region trapped by C-shape
2. Acceleration by propulsive stroke
Midline kinematics
()*+,*-./01 2"3$ 2"3# 2"3" 2" 2!345/1-67,05-./0
MACHINE
LEARNING
STOCHASTICS
Learning
toControl
•Behavioral Traits - Vortex Dynamics •Energetic benefits ?FISH SCHOOLING
Hydromechanics of Fish Schooling
Daniel Weihs
•Vortex Dynamics AND/OR Behavioral Traits? •Energy, Predation benefits ?Title Text
Simple model of fish schooling: a leader and follower Re=L 2 /T != 5000 L TTrailing fish's head intercepts
positive vorticity: velocity increases leading fishtrailing fishSwimming in a wake can be
beneficial or detrimentalTrailing fish's head intercepts
negative vorticity velocity decreasesInitial tail-to-head distance = 1 L
Initial tail-to-head distance = 1.25 L
Learning: Behavioral changes due to
Experiences (Action, Stimulus, Reward)
Reinforcement: stimulus-action pattern is
rewarded -> actor is conditioned to a behavior.Reinforcement Learning
CREDIT: B.F. Skinner Foundation
RL in Psychology: Conditioning
Ivan Pavlov - 1890
Psychology
- Behaviorism and Decision Making -I. Pavlov, R.F. Skinner
Mathematics
- Dynamic Programming -P.J. Werbos, D. Bertsekas, J. Tsitsiklis
Economics
- Game Theory -John von Neumann
Computer Science
- Algorithms and Deep Networks -A. Barto, R. Sutton,
xyy#$%&'(Reinforcement Learning: Over 150 years of history
I. DYNAMIC PROGRAMMING ->REINFORCEMENT LEARNING
Markov Decision Processes
X t 1 =F(X t t t actionTHE FLOW SOLVER
J=! T t 1 t 2 s t tExpected
Utility
rewardTHE COST FUNCTION
s t =(X t tObservation/State
stateTHE DATA
REINFORCEMENT LEARNING:
observed BUT not known -SAMPLINGD=(,2)
STATEII. Reinforcement Learning:
Find Policy to Maximize Long Term Reward
STATEENVIRONMENT
Gaussian
s t w mean action std of action *+,"-."(/010"2"3"%."(2"41-'
516"(730%8&
AGENT m(s t x (s t w parametersDeep NN
J w a t w a!s t st+1$%(s!at,st) t r t REINFORCEMENT LEARNING : An agent learning an action policy trough rewardsGoal: Maximize the
value function action state rewardENVIRONMENT
AGENT V w s aquotesdbs_dbs41.pdfusesText_41[PDF] acrosport bac 2017
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