PhD student in AI @ Lyon 1 University
I am a third-year PhD student at Lyon 1 University, advised by Laëtitia Matignon and affiliated with the LIRIS laboratory. My research sits at the intersection of multi-objective reinforcement learning and machine ethics, with a focus on designing agents that behave in ethically aligned ways.
Research
Utility-based soft masking for continual multi-objective reinforcement learning
AAMAS 2026
* also presented at the 2026 MAFTEC workshop
Real-world decision-making usually involves balancing multiple and sometimes conflicting objectives, according to user preferences that can be complex and potentially non-linear. These preferences, or utilities, can also be subject to change over time, requiring continual adaptation. This challenge is at the center of continual multi-objective reinforcement learning (CMORL), and remains vastly understudied, with existing work limited to linear utilities. In this paper, we take a first step towards CMORL with non-linear utilities by proposing utility-based soft masking (UBSM). By generating a discretized representation of the utility and using it to soft-mask the policy's parameters, UBSM harnesses the structure of utility functions, allowing for greater knowledge transfer among them and supporting the learning of policies that adapt to dynamic preferences. We evaluate UBSM on classic multi-objective reinforcement learning environments, demonstrating its improvements over baselines and providing insights on the evaluation of CMORL algorithms.
Multi-objective reinforcement learning: an ethical perspective
Multi-Objective Decision Making workshop, ECAI 2024
* also presented at RJCIA 2024
Reinforcement learning (RL) is becoming more prevalent in practical domains with human implications, raising ethical questions. Specifically, multi-objective RL has been argued to be an ideal framework for modeling real-world problems and developing human-aligned artificial intelligence. However, the ethical dimension remains underexplored in the field, and no survey covers this aspect. Hence, we propose a review of multi-objective RL from an ethical perspective, highlighting existing works, gaps in the literature, important considerations, and potential areas for future research.
TDMD: A Database for Dynamic Color Mesh Quality Assessment Study
IEEE TVCG
* internship at Tencent Media Lab
Dynamic colored meshes (DCM) are widely used in various applications. However, this kind of meshes may undergo different processes, such as compression or transmission, which can distort them and degrade their quality. To facilitate the development of objective metrics for DCMs and study the influence of typical distortions on their perception, we create the Tencent - Dynamic colored Mesh Database (TDMD) containing eight reference DCM objects with six typical distortions. Using processed video sequences (PVS) derived from the DCM, we conduct a large-scale subjective experiment that resulted in 303 distorted DCM samples with mean opinion scores, making the TDMD the largest available DCM database to our knowledge. This database enables us to study the impact of different types of distortion on human perception and offers recommendations for DCM compression and related tasks. Additionally, we have evaluated three types of state-of-the-art objective metrics on the TDMD, including image-based, point-based, and video-based metrics, on the TDMD. Our experimental results highlight the strengths and weaknesses of each metric, and we provide suggestions about the selection of metrics in practical DCM applications.
Teaching
Algorithms, Programming and Complexity
Teaching assistant · BSc
Introduction to Deep Learning
Lecturer · MSc AI · MSc DISS (English-taught) · Polytech Lyon (engineering school)
Teaching assistant · MSc AI
Applications of Mathematics and Computer Science
Teaching assistant · BSc
Introduction to Imperative Programming
Teaching assistant · BSc