- design_functionThe desired value to reach.
C++ Type:FunctionName
Unit:(no unit assumed)
Controllable:No
Description:The desired value to reach.
- observed_valueThe name of the Postprocessor that contains the observed value.
C++ Type:PostprocessorName
Unit:(no unit assumed)
Controllable:No
Description:The name of the Postprocessor that contains the observed value.
ScaledAbsDifferenceDRLRewardFunction
Evaluates a scaled absolute difference reward function for a process which is controlled by a Deep Reinforcement Learning based surrogate.
Overview
Function describing the reward of for a Deep Reinforcement Learning algorithm in the form of:
where and constants can be determined by the user. Furthermore, is a measured data, typically supplied by a postprocessor. For an example on how to use it in a DRL setting, see LibtorchDRLControlTrainer.
Input Parameters
- c1101st coefficient in the reward function.
Default:10
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:1st coefficient in the reward function.
- c212nd coefficient in the reward function.
Default:1
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:2nd coefficient in the reward function.
Optional Parameters
- control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
- enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Controllable:No
Description:Set the enabled status of the MooseObject.