Portrait de Ayoub Echchahed

Ayoub Echchahed

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Pablo Samuel Castro
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage profond
Site web

Publications

A Survey of State Representation Learning for Deep Reinforcement Learning
Ayoub Echchahed
Pablo Samuel Castro
Representation learning methods are an important tool for addressing the challenges posed by complex observations spaces in sequential decis… (voir plus)ion making problems. Recently, many methods have used a wide variety of types of approaches for learning meaningful state representations in reinforcement learning, allowing better sample efficiency, generalization, and performance. This survey aims to provide a broad categorization of these methods within a model-free online setting, exploring how they tackle the learning of state representations differently. We categorize the methods into six main classes, detailing their mechanisms, benefits, and limitations. Through this taxonomy, our aim is to enhance the understanding of this field and provide a guide for new researchers. We also discuss techniques for assessing the quality of representations, and detail relevant future directions.
2025-06-23
TMLR (accepté)
openreview.net
openreview.net
Towards Sustainable Investment Policies Informed by Opponent Shaping
Juan Agustin Duque
Razvan Ciuca
Ayoub Echchahed
Hugo Larochelle
Aaron Courville
Addressing climate change requires global coordination, yet rational economic actors often prioritize immediate gains over collective welfar… (voir plus)e, resulting in social dilemmas. InvestESG is a recently proposed multi-agent simulation that captures the dynamic interplay between investors and companies under climate risk. We provide a formal characterization of the conditions under which InvestESG exhibits an intertemporal social dilemma, deriving theoretical thresholds at which individual incentives diverge from collective welfare. Building on this, we apply Advantage Alignment, a scalable opponent shaping algorithm shown to be effective in general-sum games, to influence agent learning in InvestESG. We offer theoretical insights into why Advantage Alignment systematically favors socially beneficial equilibria by biasing learning dynamics toward cooperative outcomes. Our results demonstrate that strategically shaping the learning processes of economic agents can result in better outcomes that could inform policy mechanisms to better align market incentives with long-term sustainability goals.
2025-06-23
rl-conference.cc/RLC/2025/Workshop/CoCoMARL (poster)
openreview.net
openreview.net