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math.OC2025
Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
Donglin Zhan, Leonardo F. Toso, James Anderson
We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically assumes that all available tasks are equ…
math.OC2024
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR
Leonardo F. Toso, Donglin Zhan, James Anderson +1
We investigate the problem of learning linear quadratic regulators (LQR) in a multi-task, heterogeneous, and model-free setting. We characterize the stability and personalization g…