Hello Jakob,
Given the following from the link you pointed out:
"This random number generator (RNG) is initialized with a seed value which defaults to the (arbitrary) value 23423.
This setup makes all applications deterministic by default as the sequence of random numbers is fixed for a given seed.
The seed may be changed using the option --seed <INT>. When using the option --random the seed will be chosen based on the current system time resulting in truly random behavior."
I have two questions:
First, I had the seed set to this: seed = np.random.randint(10000), so the deterministic, totally reproducible behavior in certain settings, shouldn't be expected.
Second, even if I hadn't chosen a random one, and let it to the default 23423 stated above instead, I still don't get why this is fixed for always when I run scenario A, and then B at once, and results in always the same behavior, but it is different when I run only scenario B over and over, to get always the same behavior, but different than the one in A.
(1. Run A than reset Sumo (not restart), then run B ---> no matter how often I run this I get exactly the same behavior,
2. Run B ---> no matter how often I run this I get exactly the same behavior but slightly different than the one in scenario B in 1.)
To me this two make sense:
either I get the same behavior whenever I run scenario B regardless if I run it on its own or before/after other scenarios, or I always(or sometimes) get a slightly different ones due to a random seed (but also within runs of A,B or just B).
Can you please explain what am I missing here?
Thanks!
VG
Branka