Raphael Avalos

Raphael Avalos

PhD Candidate

AI Lab VUB

Biography

Hello! I am a PhD student at the AI LAB of VUB (University of Brussels), where I am supervised by Professors Ann Nowe and Diederik Roijers. The central focus of my research is the challenge of partial observability in both single-agent and multi-agent reinforcement learning (RL). My approach to addressing this issue is centered on leveraging additional data during the learning phase and potentially incorporating communication at the execution stage. My current main interests are in model-based RL and exploration strategies I welcome academic discussions, collaborations, or professional inquiries related to these areas. Feel free to contact me.

Download my resumé.

Interests
  • Artificial Intelligence
  • Reinforcement Learning
  • Partially Observable
  • Multi-Agent RL (collaboration, communication)
  • Model Based RL
  • Exploration
Education

  • MSc in Applied Mathematics for AI, 2019

    Ecole Normale Superieur de Cachan

  • MSE in Computer Science, 2019

    Telecom Paris

Experience

 
 
 
 
 
Program Chair
Adaptive and Learning Agents (ALA2024)
Oct 2023 – May 2024 Auckland, New Zeland
 
 
 
 
 
Local Chair
European Workshop in Reinforcement Learning (EWRL 2023)
Jan 2023 – Sep 2023 Brussels, Belgium
 
 
 
 
 
Vrije Universiteit Brussel (VUB) - AI Lab - FWO
PhD Candidate
Vrije Universiteit Brussel (VUB) - AI Lab - FWO
May 2020 – Present Brussels, Belgium
PhD supervised by Prof. Ann Nowe and Dr. Diederik Roijers.
 
 
 
 
 
Vrije Universiteit Brussel (VUB) - AI Lab
Research Intern
Vrije Universiteit Brussel (VUB) - AI Lab
Jan 2020 – Apr 2020 Brussels, Belgium
Working on Multi-agent Reinforcement Learning under the supervision of Prof. Ann Nowe.
 
 
 
 
 
INRIA - SequeL Team (now Scool)
Research Intern
INRIA - SequeL Team (now Scool)
Apr 2019 – Oct 2019 Lille, France
Worked on Hierarchical Reinforcement Learning (HRL) applied to the Rubik’s Cube under the supervision Dr. Florian Strub and Prof. Philippe Preux.

Accomplish­ments

PhD Fellowship fundamental research
FWO - Research Foundation - Flanders Oct 2020 – Sep 2024

Publications Journal

Raphael Avalos, Mathieu Reymond, Ann Nowe, Diederik M. Roijers (2022). Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. TMLR.

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Publications Conference

Yannick Molinghen, Raphael Avalos, Mark Van Achter, Ann Nowe, Tom Lenaerts (2023). Laser Learning Environment: A new environment for coordination-critical multi-agent tasks. BNAIC 2023.

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Raphael Avalos (2022). Exploration and Communication for Partially Observable Collaborative Multi-Agent Reinforcement Learning. AAMAS 2022.

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Raphael Avalos, Mathieu Reymond, Ann Nowe, Diederik M. Roijers (2022). Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. AAMAS 2022.

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Publications Workshop

Raphael Avalos, Mathieu Reymond, Ann Nowe, Diederik M. Roijers (2022). Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. EWRL 2022.

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Andries Rosseau, Raphael Avalos, Ann Nowe (2022). Autocurricula and Emergent Sociality from a Gene Perspective. ALA 2022 (AAMAS Workshop).

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Eugenio Bargiacchi, Raphael Avalos, Timothy Verstraeten, Pieter Libin, Ann Nowe, Diederik M. Roijers (2022). Multi-agent RMax for Multi-Agent Multi-Armed Bandits. ALA 2022 (AAMAS Workshop).

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Raphael Avalos, Florent Delgrange, Ann Nowe, Guillermo Perez, Diederik M. Roijers (2017). The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. EWRL 2023.

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Publications Preprint

Qingshuang Sun, Denis Steckelmacher, Yuan Yao, Ann Nowe, Raphael Avalos (2017). Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth. Preprint.

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