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Gaspard Lambrechts

Postdoctorat - McGill
Superviseur⋅e principal⋅e
Aditya Mahajan
Co-supervisor
Pierre-Luc Bacon
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Modèles génératifs
Réseaux de neurones récurrents
Systèmes dynamiques
Théorie de l'information
Site web
Google Scholar
GitHub

Publications

A Theoretical Justification for Asymmetric Actor-Critic Algorithms
Gaspard Lambrechts
Damien Ernst
Aditya Mahajan
In reinforcement learning for partially observable environments, many successful algorithms have been developed within the asymmetric learni… (voir plus)ng paradigm. This paradigm leverages additional state information available at training time for faster learning. Although the proposed learning objectives are usually theoretically sound, these methods still lack a precise theoretical justification for their potential benefits. We propose such a justification for asymmetric actor-critic algorithms with linear function approximators by adapting a finite-time convergence analysis to this setting. The resulting finite-time bound reveals that the asymmetric critic eliminates error terms arising from aliasing in the agent state.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press