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Gaspard Lambrechts

gaspard.lambrechts@mila.quebec
Postdoctorate - McGill University
Supervisor
Aditya Mahajan
Co-supervisor
Pierre-Luc Bacon
Research Topics
Dynamical Systems
Generative Models
Information Theory
Recurrent Neural Networks
Reinforcement Learning
Representation Learning
Website
Google Scholar
GitHub

Publications

Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance
Gaspard Lambrechts
Adrien Bolland
Daniel Ebi
Damien Ernst
Much like humans benefit from guidance while learning, reinforcement learning algorithms may benefit from additional supervision beyond rewa… (see more)rds. Leveraging additional information during training to learn better representations and behaviors has been the focus of asymmetric reinforcement learning. This learning paradigm has proven effective under partial observability when additional state information is available, but also under full observability when more refined state information is available. Focusing on model-based reinforcement learning, we study the effect of asymmetric learning on observation representations and on privileged information representations. First, we identify a limitation in the privileged information representations learned by an asymmetric model-based algorithm known as the Informed Dreamer. Then, we propose a novel asymmetric representation learning objective using latent guidance, resulting in a new algorithm called the Reinformed Dreamer. Experiments across several benchmarks show a more consistent improvement over Dreamer than previous asymmetric approaches.
2026-07-27
arXiv (preprint)
doi.org
arxiv.org
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… (see more)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 (published)
doi.org
proceedings.mlr.press