MSPBE: Objective functions for Q-learning
In XiaoqiLu/PhD-Thesis: Regularized Q-Learning
Description
Usage
Arguments
Value
Functions
Description
Objective functions for Q-learning
Usage
1
2
3
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
a (non-negative) number
Functions
-
MSPBE: Mean Squared Projected Bellman Error
-
MSBE: Mean Squared Bellman Error
XiaoqiLu/PhD-Thesis documentation built on March 1, 2021, 10:49 a.m.
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Description Usage Arguments Value Functions
Description
Objective functions for Q-learning
Usage
1 2 3 |
Arguments
theta |
a numeric vector as model parameter. |
phis |
a list of processed outcome from |
discount |
a numeric number between 0 and 1. |
Value
a (non-negative) number
Functions
-
MSPBE: Mean Squared Projected Bellman Error -
MSBE: Mean Squared Bellman Error
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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