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On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency
In several, there is no information loss and value-based methods are as statistically efficient as model based ones.
On the Statistical Benefits of Temporal Difference Learning
Given a dataset on actions and resulting long-term rewards, a direct estimation approach fits value functions that minimize prediction error on the training data.
Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometrically-driven optimization algorithms on the orthogonal group $O(d)$ and naturally reductive homogeneous manifolds obtained from the action of the rotation group $SO(d)$.
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