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Found 2 papers, 1 papers with code
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
After introducing a novel framework for modeling this scenario, we study the global convergence to the best deterministic policy, under (weak) gradient domination assumptions.
Best Arm Identification for Stochastic Rising Bandits
Then, we prove that, with a sufficiently large budget, they provide guarantees on the probability of properly identifying the optimal option at the end of the learning process.
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