arXiv subjects

Philippe Hamel

Publications and source records attributed to Philippe Hamel.

10 recordsLinked to original sources

arXiv2024

TORAX: A Fast and Differentiable Tokamak Transport Simulator in JAX

We present TORAX, a new, open-source, differentiable tokamak core transport simulator implemented in Python using the JAX framework. TORAX solves the coupled equations for ion heat transport, electron heat transport, particle transport, and current diffusion, incorporating modular physics-based and ML models. JAX's just-in-time compilation ensures fast runtimes, while its automatic differentiation capability enables gradient-based optimization workflows and simplifies the use of Jacobian-based PDE solvers. Coupling to ML-surrogates of physics models is greatly facilitated by JAX's intrinsic support for neural network development and inference. TORAX is verified against the established RAPTOR code, demonstrating agreement in simulated plasma profiles. TORAX provides a powerful and versatile tool for accelerating research in tokamak scenario modeling, pulse design, and control.

physics.plasm-ph↗

arXiv2022

Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke the solutions of lower-level tasks as if they were primitive actions. In this work, we study the utility of hierarchical decompositions for learning an appropriate way to interact with a complex interface. Specifically, we train HRL agents that can interface with applications in a simulated Android device. We introduce a Hierarchical Distributed Deep Reinforcement Learning architecture that learns (1) subtasks corresponding to simple finger gestures, and (2) how to combine these gestures to solve several Android tasks. Our approach relies on goal conditioning and can be used more generally to convert any base RL agent into an HRL agent. We use the AndroidEnv environment to evaluate our approach. For the experiments, the HRL agent uses a distributed version of the popular DQN algorithm to train different components of the hierarchy. While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving the performance of the same DQN agent over different levels of abstraction.

cs.LG↗

arXiv2021

The Option Keyboard: Combining Skills in Reinforcement Learning

The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended periods. We argue that a robust way of combining skills is to define and manipulate them in the space of pseudo-rewards (or "cumulants"). Based on this premise, we propose a framework for combining skills using the formalism of options. We show that every deterministic option can be unambiguously represented as a cumulant defined in an extended domain. Building on this insight and on previous results on transfer learning, we show how to approximate options whose cumulants are linear combinations of the cumulants of known options. This means that, once we have learned options associated with a set of cumulants, we can instantaneously synthesise options induced by any linear combination of them, without any learning involved. We describe how this framework provides a hierarchical interface to the environment whose abstract actions correspond to combinations of basic skills. We demonstrate the practical benefits of our approach in a resource management problem and a navigation task involving a quadrupedal simulated robot.

cs.AI↗

arXiv2021

AndroidEnv: A Reinforcement Learning Platform for Android

We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to interact with a wide variety of apps and services commonly used by humans through a universal touchscreen interface. Since agents train on a realistic simulation of an Android device, they have the potential to be deployed on real devices. In this report, we give an overview of the environment, highlighting the significant features it provides for research, and we present an empirical evaluation of some popular reinforcement learning agents on a set of tasks built on this platform.

cs.LG↗

arXiv2017

Quench dynamics in strongly coupled laser cavities

Strongly coupled dissipative optical cavities with nonlinear interactions give new opportunities to explore symmetry breaking phenomena and phase transitions, Josephson dynamics and quantum criticality. Among the different experimental realizations, photonic crystal coupled nanocavities operating in the laser regime are outstanding systems since nonlinearity, gain/dissipation and intercavity coupling can be judiciously tailored. Yet, although most common scenarios emerge from quasi-dynamical equilibrium where the gain nearly compensates for losses, little is known about far out-of-equilibrium dynamics resulting, for instance, from short pulsed pumping inducing a classical "quench". Here we show that bimodal nanolasers generically display transient dynamics after quench which, when projected onto the nonlasing mode, exhibit superthermal light. Such a mechanism is akin to the fast cooling of a suspension of Brownian particles under gravity, with the intracavity intensity playing the role of the inverse temperature. We implement a simple experimental technique to access the probability density functions, that enabled quantifying the distance from thermal equilibrium --and hence the degree of residual order-- via the Gibbs entropy. This allowed us to further detect mixing of thermal states with coherent broken parity phases, thus paving the road for investigating far nonequilibrium thermodynamics with multimode optical oscillators.

physics.optics↗

arXiv2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.

cs.SC↗

arXiv2014

Spontaneous mirror-symmetry breaking in a photonic molecule

Multi-cavity photonic systems, known as photonic molecules (PMs), are ideal multi-well potential building blocks for advanced quantum and nonlinear optics. A key phenomenon arising in double well potentials is the spontaneous breaking of the inversion symmetry, i.e. a transition from a delocalized to two localized states in the wells, which are mirror images of each other. Although few theoretical studies have addressed mirror-symmetry breaking in micro and nanophotonic systems, no experimental evidence has been reported to date. Thanks to the potential barrier engineering implemented here, we demonstrate spontaneous mirror-symmetry breaking through a pitchfork bifurcation in a PM composed of two coupled photonic crystal nanolasers. Coexistence of localized states is shown by switching them with short pulses. This offers exciting prospects for the realization of ultra-compact, integrated, scalable optical flip-flops based on spontaneous symmetry breaking. Furthermore, we predict such transitions with few intracavity photons for future devices with strong quantum correlations.

physics.optics↗

arXiv2011

Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint Semantic Spaces

Music prediction tasks range from predicting tags given a song or clip of audio, predicting the name of the artist, or predicting related songs given a song, clip, artist name or tag. That is, we are interested in every semantic relationship between the different musical concepts in our database. In realistically sized databases, the number of songs is measured in the hundreds of thousands or more, and the number of artists in the tens of thousands or more, providing a considerable challenge to standard machine learning techniques. In this work, we propose a method that scales to such datasets which attempts to capture the semantic similarities between the database items by modeling audio, artist names, and tags in a single low-dimensional semantic space. This choice of space is learnt by optimizing the set of prediction tasks of interest jointly using multi-task learning. Our method both outperforms baseline methods and, in comparison to them, is faster and consumes less memory. We then demonstrate how our method learns an interpretable model, where the semantic space captures well the similarities of interest.

cs.LG↗

arXiv2005

Polarization States in B -> rho K* and New Physics

The standard-model explanations of the anomalously-large transverse polarization fraction fT in B -> phi K* can be tested by measuring the polarizations of the two decays B+ -> rho+ K*0 and B+ -> rho0 K*+. For the scenario in which the transverse polarizations of both B -> rho K* decays are predicted to be large, we derive a simple relation between the fT's of these decays. If this relation is not confirmed experimentally, this would yield an unambiguous signal for new physics. The new-physics operators which can account for the discrepancy in B -> pi K decays will also contribute to the polarization states of B -> rho K*. We compute these contributions and show that there are only two operators which can simultaneously account for the present B -> pi K and B -> rho K* data. If the new physics obeys an approximate U-spin symmetry, the B -> phi K* measurements can also be explained.

hep-ph↗

arXiv2004

The B -> pi K Puzzle and New Physics

The present B -> pi K data is studied in the context of the standard model (SM) and with new physics (NP). We confirm that the SM has difficulties explaining the B -> pi K measurements. By adopting an effective-lagrangian parametrization of NP effects, we are able to rule out several classes of NP. Our model-independent analysis shows that the B -> pi K data can be accommodated by NP in the electroweak penguin sector.

hep-ph↗