Yanwei Jia

Convergence and Regret of the Policy Gradient for Multi-Armed Bandits in Diffusion Environment

The logit parameterization for the stochastic policy converges almost surely to the optimal arm under an arbitrary constant learning rate and derives the non-asymptotic regret upper bound when the constant learning rate is below a time-invariant threshold.