Manifold-regularized/preconditioned methods Clause Samples

Manifold-regularized/preconditioned methods. ‌ The purpose of this section is to explore the motivation, development, and current state of second-order optimization in its application to policy-gradient methods in reinforcement learning. In this regard, the theoretical foundations of manifold learning come from the field of information geometry i.e. the study of statistical manifolds. A ubiquitous feature within this field is the construction of an approximations for metric tensor on the manifold of parameters for the purpose of deriving an improved descent iteration as an alternative to stochastic gradient descent. Such efforts are motivated in a similar fashion as the Newton method in contrast to gradient descent

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