Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for the Model-free LQR
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 guarantees of a Policy Gradient-based (PG) Model-Agnostic Meta-Learning (MAML) (Finn et al., 2017) approach for the LQR problem under different task-heterogeneity settings. We show that the MAML-LQR approach produces a stabilizing controller close to each task-specific optimal controller up to a task-heterogeneity bias for both model-based and model-free settings. Moreover, in the model-based setting, we show that this controller is achieved with a linear convergence rate, which improves upon sub-linear rates presented in existing MAML-LQR work. In contrast to existing MAML-LQR results, our theoretical guarantees demonstrate that the learned controller can efficiently adapt to unseen LQR tasks.
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