function approximationの例文

• Function approximation is another way to solve the mountain car.
• Neural networks also started to be used as a general function approximation model.
• This spectral framework can be used for value function approximation ( VFA ).
• More generally, " Q "-learning can be combined with function approximation.
• The logistic map can be used to explore function approximation, time series prediction, and control theory.
• Reinforcement learning can also be combined with function approximation to address problems with a very large number of states.
• Greedy GQ is a variant of " Q "-learning to use in combination with ( linear ) function approximation.
• The advantage of Greedy GQ is that convergence guarantees can be given even when function approximation is used to estimate the action values.
• Proto-value functions arise from reformulating the problem of value function approximation as real-valued function approximation on a graph or manifold.
• Proto-value functions arise from reformulating the problem of value function approximation as real-valued function approximation on a graph or manifold.
• Each step of training has a wider impact on the value function approximation because when the offset grids are summed, the information is diffused.
• Binomial probability mass function and normal probability density function approximation for " n " = 6 and " p " = 0.5
• Tasks that fall within the paradigm of supervised learning are pattern recognition ( also known as classification ) and regression ( also known as function approximation ).
• Then, the coefficients of the function approximations are treated as optimization variables and the problem is " transcribed " to a nonlinear optimization problem of the form:
• Unlike the step-wise version of the value function created with discretization, function approximation can more cleanly estimate the true smooth function of the mountain car domain.
• In a direct method, the state and / or control are approximated using an appropriate function approximation ( e . g ., polynomial approximation or piecewise constant parameterization ).
• An approximate identity in a convolution algebra plays the same role as a sequence of function approximations to the Dirac delta function ( which is the identity element for convolution ).
• It is of note that the notion of spectral filtering studied in the context of machine learning is closely connected to the literature on function approximation ( in signal processing ).
• If the purpose is not to perform strict interpolation but instead more general function approximation or classification the optimization is somewhat more complex because there is no obvious choice for the centers.
• In different research, such as signal processing and function approximation, overcompleteness can help researchers to achieve a more stable, more robust, or more compact decomposition than using a basis.