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ProvablyEfficientModel-FreeConstrainedRLwith LinearFunctionApproximation
We study the constrained reinforcement learning problem, in which an agent aims tomaximize the expected cumulativereward subject toaconstraint on the expected total value of a utility function. In contrast to existing model-based approaches or model-free methods accompanied with a'simulator', we aim to develop thefirst model-free, simulator-freealgorithm that achieves a sublinear regret and a sublinear constraint violation even inlarge-scale systems.
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- North America > United States > Ohio > Franklin County > Columbus (0.04)
- South America > Chile > Santiago Metropolitan Region > Santiago Province > Santiago (0.04)
- North America > United States > Michigan > Wayne County > Detroit (0.04)
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