GradientFQI: Gradient functions for Q-learning
In XiaoqiLu/PhD-Thesis: Regularized Q-Learning
Description
Usage
Arguments
Value
Functions
Description
Gradient functions for Q-learning
Usage
1
2
3
4
5
GradientFQI(theta, phis, discount)
GradientGGQ(theta, phis, discount)
GradientBEM(theta, phis, discount)
Arguments
theta
a numeric vector as model parameter.
phis
a list of processed outcome from SARS2Phis().
discount
a numeric number between 0 and 1.
Value
gradient
Functions
-
GradientFQI: Fitted Q Iteration (objective function: MSPBE)
-
GradientGGQ: Greedy Gradient-Q (objective function: MSPBE)
-
GradientBEM: Bellman Error Minimization (objective function: MSBE)
XiaoqiLu/PhD-Thesis documentation built on March 1, 2021, 10:49 a.m.
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Description Usage Arguments Value Functions
Description
Gradient functions for Q-learning
Usage
1 2 3 4 5 | GradientFQI(theta, phis, discount)
GradientGGQ(theta, phis, discount)
GradientBEM(theta, phis, discount)
|
Arguments
theta |
a numeric vector as model parameter. |
phis |
a list of processed outcome from |
discount |
a numeric number between 0 and 1. |
Value
gradient
Functions
-
GradientFQI: Fitted Q Iteration (objective function: MSPBE) -
GradientGGQ: Greedy Gradient-Q (objective function: MSPBE) -
GradientBEM: Bellman Error Minimization (objective function: MSBE)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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