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Traditional Reinforcement Learning (RL) controllers are based on a discrete formulation of the Dynamic Programming (DP) problem, which impedes the development of rigorous stability analysis of continuous-time closed loop controllers for uncertain nonlinear systems. Non-DP based RL controllers typically yield a uniformly ultimately bounded (UUB) stability result due to the presence of disturbances...
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