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Convergence of Gradient-based MAML in LQR
The main objective of this research paper is to investigate the local convergence characteristics of Model-agnostic Meta-learning (MAML) when applied to linear system quadratic optimal control (LQR).
Verification and Parameter Synthesis for Stochastic Systems using Optimistic Optimization
As a consequence, we obtain theoretical regret bounds on sample efficiency of our solution that depends on key problem parameters like smoothness, near-optimality dimension, and batch size.
A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts
The contribution of this work is to propose a modeling framework, in which, human pilot reactions are modeled using reinforcement learning and a game theoretical concept called level-k reasoning to fill this gap.
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