Get.Def.Par.Neural.Network: Define default Parameters of the Neural Network Function
In MartinKies/USLR: Reinforcement Learning with R

Returns a List which may be used as model.par of e.g. the function Setup.QLearning() with the following parameters:

name - Identifier of Model. Per Default \"Neural.Network.Basic\".
setup - Function which should be used to setup the Neural Network. Per Default Setup.Neural.Network
predict - Function which should be used to predict the Neural Network. Per Default Predict.Neural.Network
train - Function which should be used to train/calibrate the Neural Network. Per Default Train.Neural.Network
hidden.nodes - A Vector consisting of the number of Neurons in each hidden layer - e.g. c(25,10) to have two hidden layers with the first layer having 25 Neurons.
activation.hidden - A Vector defining the activation functions of the hidden layers, e.g. c(\"relu\",\"relu\"). Has to have the same number of items as hidden.nodes. Supported are e.g. relu, tanh, sigmoid and linear
activation.output. Activiation function of the output layer. Supported are e.g. relu, tanh, sigmoid and linear.
loss. Specifies the loss function, e.g. \'mse\'
optimizer. Specifies the used optimizer. By Default Adam Optimization is used with a Learning rate of 0.001.
epochs. How many epochs should the Neural Network be trained?
batch.size. Batch Size of Neural Network.
verbose. Should the Neural Network give an output? 0 for no output, 1 for output for each epoch, 2 for aggregate output every other epoch.