Noise model in RL for large action signal

I want to train a model with an DDPG agent. The model requires an action 10 element vetor signal with a bound value of -1.5...+1.5. The problem is, that the noise model seems to only change 5 elements per episode, while the other signals stay at the upper or lower limits. The noise is added to all signals over time, but it seems that only approx. 5 signals are changed at once. Example action signal that I get:

[1.5 0.2 0.4 -0.1 -1.5 -1.5 1.5 0.2 0.4 -0.1 -1.5]

If the noise does never change all signal elements together, the system will never find the spot, at which the system becomes stable. I let the training run for over 24h, without any change of the noise behavior. Is this a known thing and is there a way to alter the noise behavior? It did try to change the NoiseOptions parameters, but this does not change the way, how the noise affects the general problem I have, which still keeps always approx. 5 signals at their respective upper or lower bounds.