Error using deep.internal.network.dlnetwork/setLearnables
Layer 'fc': Invalid Learnables. Expected input to be of size [6 59], but it is of size [1 59].
The reason for the error you're encountering is likely due to how you've set up the learning architecture, resembling a control architecture. In linear control systems, the matrix differential equation can be expressed in the vector field form known as the state-space system:
.Here, '
' is a mathematically state-dependent function, something like f(x), referred to as the state-feedback control input, and it's given by:
' is a mathematically state-dependent function, something like f(x), referred to as the state-feedback control input, and it's given by:
.When we substitute '
' into the state-space equation, it becomes:
' into the state-space equation, it becomes:
.This can be simplified by factoring the state vector '
':
':
.Now, the equation size is perfectly conserved.
From the perspective of the RL agent, it expects the learnable parameters to be of size 
, which actually represents the LQR gain matrix '
'. This is because the control vector '
', which has a size of 
, is already assimilated into the state-space as the LQR, taking the form of state-feedback.
, which actually represents the LQR gain matrix ''. This is because the control vector '', which has a size of , is already assimilated into the state-space as the LQR, taking the form of state-feedback.
