Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider

1 code implementation • 16 May 2021 • Anna Stakia, Tommaso Dorigo, Giovanni Banelli, Daniela Bortoletto, Alessandro Casa, Pablo de Castro, Christophe Delaere, Julien Donini, Livio Finos, Michele Gallinaro, Andrea Giammanco, Alexander Held, Fabricio Jiménez Morales, Grzegorz Kotkowski, Seng Pei Liew, Fabio Maltoni, Giovanna Menardi, Ioanna Papavergou, Alessia Saggio, Bruno Scarpa, Giles C. Strong, Cecilia Tosciri, João Varela, Pietro Vischia, Andreas Weiler

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones.

Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling

no code implementations • 28 Apr 2020 • Dano Roost, Ralph Meier, Stephan Huschauer, Erik Nygren, Adrian Egli, Andreas Weiler, Thilo Stadelmann

We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and two hypotheses with respect to the prospect of deep RL for complex real-world control tasks: first, that current state of the art policy gradient methods seem inappropriate in the domain of high-consequence environments; second, that learning explicit communication actions (an emerging machine-to-machine language, so to speak) might offer a remedy.

Policy Gradient Methods reinforcement-learning +1