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Elementary Analysis of Policy Gradient Methods
Projected policy gradient under the simplex parameterization, policy gradient and natural policy gradient under the softmax parameterization, are fundamental algorithms in reinforcement learning.
On the Linear Convergence of Policy Gradient under Hadamard Parameterization
To show the local linear convergence of the algorithm, we have indeed established the contraction of the sub-optimal probability $b_s^k$ (i. e., the probability of the output policy $\pi^k$ on non-optimal actions) when $k\ge k_0$.
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