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The resulting parametric PDEs are solved by reduced basis methods.
Both the Agent and the 'Action basis' methods have explicitly specified target policies.
Numerical reduced basis methods are instrumental to solve parameter dependent partial differential equations problems in case of many queries.
For a general introduction on the development of the reduced basis methods we refer to [1 3].
Of the three other basis methods, the most advantageous taxwise (and probably the only other one you'll need to know about) is the "specified share" method.
A learning rate of 0.0005 was used for the Agent and Action basis methods, and 0.0001 for the two REINFORCE methods.
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Oh, about that fourth basis method.
Section 3 discusses the numerical reduced basis method.
The reduced basis method is supported by standard discretizations.
The filters are designed using the joint best basis method.
This work provides a basis method to investigate promising building blocks for future high-density memories.
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