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Finite-time convergence rates for risk-sensitive reinforcement learning algorithms reveal that model-free approaches can achieve robust performance without complex parameter tuning.
More function measurements in random direction stochastic approximation can significantly reduce estimation bias in Hessian estimation, improving the performance of stochastic Newton methods.
