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cs.LG2026
General Machine Learning: Theory for Learning Under Variable Regimes
Aomar Osmani
We study learning under regime variation, where the learner, its memory state, and the evaluative conditions may evolve over time. This paper is a foundational and structural contr…
cs.LG2026
Meta-RL with Shared Representations Enables Fast Adaptation in Energy Systems
Théo Zangato, Aomar Osmani, Pegah Alizadeh
Meta-Reinforcement Learning addresses the critical limitations of conventional Reinforcement Learning in multi-task and non-stationary environments by enabling fast policy adaptati…
cs.LG2025
Data-Driven Policy Mapping for Safe RL-based Energy Management Systems
Theo Zangato, Aomar Osmani, Pegah Alizadeh
Increasing global energy demand and renewable integration complexity have placed buildings at the center of sustainable energy management. We present a three-step reinforcement lea…