Découverte de Politiques Interprétables pour l'Apprentissage par Renforcement via la Programmation Génétique

International audienceL'apprentissage par renforcement profond a connu un succès remarquable au cours des dernières années pour la résolution d'un large éventail de problèmes de contrôle difficiles. Les milliers de poids et non-linéarité constituant les réseaux de neurones, clef de voûte de cette approche, les rendent cependant incompréhensibles. Le présent rapport présente l'application de la programmation génétique à diverses tâches de contrôle. L'objectif de cette méthode est de produire des politiques symboliques interprétables. Tout d'abord, nous nous intéressons à la viabilité de ces politiques, aussi bien sûr le plan des performances que sur le plan de l'interprétabilité. Puis, nous explorions différentes stratégies pour échapper aux optimums locaux afin d'améliorer leurs performances. Nos résultats montrent que cette approche est une alternative crédible au réseau de neurones pour des tâches concrètes

Multi-Objective Genetic Programming for Explainable Reinforcement Learning

International audienceDeep reinforcement learning has met noticeable successes recently for a wide range of control problems. However, this is typically based on thousands of weights and non-linearities, making solutions complex, not easily reproducible, uninterpretable and heavy. The present paper presents genetic programming approaches for building symbolic controllers. Results are competitive, in particular in the case of delayed rewards, and the solutions are lighter by orders of magnitude and much more understandable

Toulouse, the Glorious Athens of the South? Computational Analysis of the Salons des Artistes Meridionaux (1907-1939)

Created in 1905, the Société des Artistes Méridionaux organized annual exhibitions in Toulouse, in order to promote regional arts and (re)created a "Latin"-and modern-style. Based on a corpus of 11,486 artworks exhibited at the SAM between 1907 and 1939, this paper seeks to measure the contours and particularities of the Salons des Artistes Méridionaux, using a computational approach

Improving Nevergrad’s Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration

International audienceAlgorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace