[question] can we make mass_spring.py repeatable (deterministic) between runs?

[ehannigan]

I was debugging some modifications I made to mass_spring.py when I realized that the result of each run is non-deterministic. I went back to the original mass_spring.py and made sure the controller network weights were initialized to the same value each time. But even when I can guarantee that there are no random variables being assigned anywhere, the resulting loss differs in each run.

Here are two different runs of the exact same code. You can see that the controller weights are exactly the same, but the loss values begin to diverge.

Run 1: mass_spring.py 2 train
n_objects= 20 n_springs= 46 weights1[0,0] -0.23413006961345673 weights2[0,0] 0.46663400530815125 Iter= 0 Loss= -0.2193218171596527 0.19502715683487248 Iter= 1 Loss= -0.21754804253578186 0.07976935930575488 Iter= 2 Loss= -0.3397877812385559 0.055776006347379746 Iter= 3 Loss= -0.3514309227466583 0.03870257399629174
Run 2: mass_spring.py 2 train
n_objects= 20 n_springs= 46 weights1[0,0] -0.23413006961345673 weights2[0,0] 0.46663400530815125 Iter= 0 Loss= -0.21932175755500793 0.1950520028177551 Iter= 1 Loss= -0.21754644811153412 0.07983238023710348 Iter= 2 Loss= -0.3397367000579834 0.055822440269175766 Iter= 3 Loss= -0.3514898419380188

In my own modifications, this was resulting in inconsistent failures of the simulation (v_inc will explode and all values will go to nan). I assume this is due to instabilities in Euler integration, but it would be nice to be able to get consistent results each time to make debugging easier.

Where could the non-deterministic behavior be coming from? Is it something we can fix, or are there stochastic processes that are a result of the compiler?