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  "author": "Sergio Rozada",
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  "author": "Sergio Rozada",
            
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  "doi": "10.57702/rhr9lbg1",
            
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  "doi_date_published": "2024-12-16",
            
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    {
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      "extra_author": "Santiago Paternain",
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      "extra_author": "Santiago Paternain",
            
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    },
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    {
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      "extra_author": "Antonio G. Marques",
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      "extra_author": "Antonio G. Marques",
            
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      "name": "reinforcement-learning",
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      "name": "reinforcement-learning",
            
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      "title": "Reinforcement Learning"
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      "title": "Reinforcement Learning"
            
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    }
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  "name": 
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trix-low-rank-value-function-approximation-in-reinforcement-learning",
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trix-low-rank-value-function-approximation-in-reinforcement-learning",
            
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  "notes": "Value-function (VF) approximation is a central problem in 
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  "notes": "Value-function (VF) approximation is a central problem in 
            
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Reinforcement Learning (RL). Classical non-parametric VF estimation 
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Reinforcement Learning (RL). Classical non-parametric VF estimation 
            
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suffers from the curse of dimensionality. As a result, parsimonious 
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suffers from the curse of dimensionality. As a result, parsimonious 
            
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parametric models have been adopted to approximate VFs in 
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parametric models have been adopted to approximate VFs in 
            
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high-dimensional spaces, with most efforts being focused on linear and 
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high-dimensional spaces, with most efforts being focused on linear and 
            
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neural-network-based approaches.",
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neural-network-based approaches.",
            
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  "title": "Tensor and Matrix Low-Rank Value-Function Approximation in 
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Reinforcement Learning",
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Reinforcement Learning",
            
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