Test Optimzation Algorithm | iterations: 300

CG	CONJUGATE_GRADIENT
LB	LBFGS
LG	LINE_GRADIENT_DESCENT
SG	STOCHASTIC_GRADIENT_DESCENT

title	Test Optimzation Algorithm | iterations: 300
class	class vsoc.training.LearningRateIterationsTraining$
learningRate	1.0E-04
trainingData	playerpos_x A 50000, playerpos_x A 100000, playerpos_x A 500000, playerpos_x A 1000000
batchSizeTrainingDataRelative	0.10
testData	playerpos_x B 1000
iterations	300
optAlgo	CONJUGATE_GRADIENT, LBFGS, LINE_GRADIENT_DESCENT, STOCHASTIC_GRADIENT_DESCENT
seed	8726567394090864187

Conclusion

Stochastic gradient descent seems to be the best optimisation. Eventually the other algorithms perform better with other meta parameters.