Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization

13 Jul 2020 Shicong Cen Chen Cheng Yuxin Chen Yuting Wei Yuejie Chi

Natural policy gradient (NPG) methods are among the most widely used policy optimization algorithms in contemporary reinforcement learning. This class of methods is often applied in conjunction with entropy regularization -- an algorithmic scheme that encourages exploration -- and is closely related to soft policy iteration and trust region policy optimization... (read more)

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Entropy Regularization