1,623 research outputs found
Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments
In this paper, we confront the problem of applying reinforcement learning to
agents that perceive the environment through many sensors and that can perform
parallel actions using many actuators as is the case in complex autonomous
robots. We argue that reinforcement learning can only be successfully applied
to this case if strong assumptions are made on the characteristics of the
environment in which the learning is performed, so that the relevant sensor
readings and motor commands can be readily identified. The introduction of such
assumptions leads to strongly-biased learning systems that can eventually lose
the generality of traditional reinforcement-learning algorithms. In this line,
we observe that, in realistic situations, the reward received by the robot
depends only on a reduced subset of all the executed actions and that only a
reduced subset of the sensor inputs (possibly different in each situation and
for each action) are relevant to predict the reward. We formalize this property
in the so called 'categorizability assumption' and we present an algorithm that
takes advantage of the categorizability of the environment, allowing a decrease
in the learning time with respect to existing reinforcement-learning
algorithms. Results of the application of the algorithm to a couple of
simulated realistic-robotic problems (landmark-based navigation and the
six-legged robot gait generation) are reported to validate our approach and to
compare it to existing flat and generalization-based reinforcement-learning
approaches
Automated microscopy for high-content RNAi screening
Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs
Pénalités minimales et heuristique de pente
International audienceBirgé and Massart proposed in 2001 the slope heuristics as a way to choose optimally from data an unknown multiplicative constant in front of a penalty. It is built upon the notion of minimal penalty, and it has been generalized since to some "minimal-penalty algorithms". This paper reviews the theoretical results obtained for such algorithms, with a self-contained proof in the simplest framework, precise proof ideas for further generalizations, and a few new results. Explicit connections are made with residual-variance estimators-with an original contribution on this topic, showing that for this task the slope heuristics performs almost as well as a residual-based estimator with the best model choice-and some classical algorithms such as L-curve or elbow heuristics, Mallows' C p , and Akaike's FPE. Practical issues are also addressed, including two new practical definitions of minimal-penalty algorithms that are compared on synthetic data to previously-proposed definitions. Finally, several conjectures and open problems are suggested as future research directions.Birgé et Massart ont proposé en 2001 l'heuristique de pente, pour déterminer à l'aide des données une constante multiplicative optimale devant une pénalité en sélection de modÚles. Cette heuristique s'appuie sur la notion de pénalité minimale, et elle a depuis été généralisée en "algorithmes à base de pénalités minimales". Cet article passe en revue les résultats théoriques obtenus sur ces algorithmes, avec une preuve complÚte dans le cadre le plus simple, des idées de preuves précises pour généraliser ce résultat au-delà des cadres déjà étudiés, et quelques résultats nouveaux. Des liens sont faits avec les méthodes d'estimation de la variance résiduelle (avec une contribution originale sur ce thÚme, qui démontre que l'heuristique de pente produit un estimateur de la variance quasiment aussi bon qu'un estimateur fondé sur les résidus d'un modÚle oracle) ainsi qu'avec plusieurs algorithmes classiques tels que les heuristiques de coude (ou de courbe en L), Cp de Mallows et FPE d'Akaike. Les questions de mise en oeuvre pratique sont également étudiées, avec notamment la proposition de deux nouvelles définitions pratiques pour des algorithmes à base de pénalités minimales et leur comparaison aux définitions précédentes sur des données simulées. Enfin, des conjectures et problÚmes ouverts sont proposés comme pistes de recherche pour l'avenir
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