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2020 – today
- 2024
[c3]Raphaël Avalos, Florent Delgrange, Ann Nowé, Guillermo A. Pérez, Diederik M. Roijers:
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. ICLR 2024- [i5]Yannick Molinghen, Raphaël Avalos, Mark Van Achter, Ann Nowé, Tom Lenaerts:
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks. CoRR abs/2404.03596 (2024) - [i4]Raphaël Avalos, Eugenio Bargiacchi, Ann Nowé, Diederik M. Roijers, Frans A. Oliehoek:
Online Planning in POMDPs with State-Requests. CoRR abs/2407.18812 (2024) - 2023
- [j1]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs. Trans. Mach. Learn. Res. 2023 (2023) - [i3]Raphaël Avalos, Florent Delgrange, Ann Nowé, Guillermo A. Pérez, Diederik M. Roijers:
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models. CoRR abs/2303.03284 (2023) - [i2]Qingshuang Sun, Denis Steckelmacher, Yuan Yao, Ann Nowé, Raphaël Avalos:
Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth. CoRR abs/2306.10134 (2023) - 2022
- [c2]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. AAMAS 2022: 1524-1526 - [c1]Raphaël Avalos:
Exploration and Communication for Partially Observable Collaborative Multi-Agent Reinforcement Learning. AAMAS 2022: 1829-1832 - 2021
- [i1]Raphaël Avalos, Mathieu Reymond, Ann Nowé, Diederik M. Roijers:
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2112.12458 (2021)
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