
Constrained Learning with NonConvex Losses
Though learning has become a core technology of modern information proce...
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Towards Safe Continuing Task Reinforcement Learning
Safety is a critical feature of controller design for physical systems. ...
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State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards
Constrained reinforcement learning involves multiple rewards that must i...
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Sufficiently Accurate Model Learning for Planning
Data driven models of dynamical systems help planners and controllers to...
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Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Prediction credibility measures, in the form of confidence intervals or ...
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Policy Gradient for Continuing Tasks in Nonstationary Markov Decision Processes
Reinforcement learning considers the problem of finding policies that ma...
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The empirical duality gap of constrained statistical learning
This paper is concerned with the study of constrained statistical learni...
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Safe Policies for Reinforcement Learning via PrimalDual Methods
In this paper, we study the learning of safe policies in the setting of ...
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Constrained Reinforcement Learning Has Zero Duality Gap
Autonomous agents must often deal with conflicting requirements, such as...
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Source Seeking in Unknown Environments with Convex Obstacles
Navigation tasks often cannot be defined in terms of a target, either be...
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Sparse multiresolution representations with adaptive kernels
Reproducing kernel Hilbert spaces (RKHSs) are key elements of many nonp...
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Learning Task Agnostic Sufficiently Accurate Models
For complex realworld systems, designing controllers are a difficult ta...
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Decentralized Online Learning with Kernels
We consider multiagent stochastic optimization problems over reproducin...
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Santiago Paternain
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