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  "author": "Jishnudeep Kar",
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    {
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      "extra_author": "He Bai",
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      "extra_author": "Aranya Chakrabortty",
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      "extra_author": "Aranya Chakrabortty",
            
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  "name": 
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ning-based-control-of-nonlinear-systems-using-carleman-approximation",
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ning-based-control-of-nonlinear-systems-using-carleman-approximation",
            
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  "notes": "We develop data-driven reinforcement learning (RL) control 
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  "notes": "We develop data-driven reinforcement learning (RL) control 
            
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designs for input-affine nonlinear systems. We use Carleman 
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designs for input-affine nonlinear systems. We use Carleman 
            
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linearization to express the state-space representation of the 
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linearization to express the state-space representation of the 
            
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nonlinear dynamical model in the Carleman space, and develop a 
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nonlinear dynamical model in the Carleman space, and develop a 
            
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real-time algorithm that can learn nonlinear state-feedback 
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real-time algorithm that can learn nonlinear state-feedback 
            
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controllers using state and input measurements in the 
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controllers using state and input measurements in the 
            
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in\ufb01nite-dimensional Carleman space.",
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in\ufb01nite-dimensional Carleman space.",
            
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  "title": "Reinforcement Learning-based Control of Nonlinear Systems 
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  "title": "Reinforcement Learning-based Control of Nonlinear Systems 
            
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using Carleman Approximation",
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using Carleman Approximation",
            
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