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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.NI2026
Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
Nicolas Helson, Pegah Alizadeh, Anastasios Giovanidis
Offline Reinforcement Learning (RL) is a promising approach for next-generation wireless networks, where online exploration is unsafe and large amounts of operational data can be r…