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!
VGBranka
Am Dienstag, 14. April 2020, 07:58:18 MESZ hat Jakob Erdmann
<[email protected]> Folgendes geschrieben:
By default the random seed is fixed to achieve the reproducible behavior you
already noticed (as explained at
https://sumo.dlr.de/docs/Simulation/Randomness.html#random_number_generation_rng).
Am Mo., 13. Apr. 2020 um 23:58 Uhr schrieb Branka Mircevska
<[email protected]>:
Hey Jakob,
thanks for the reply!
I was assuming it has to do something with the seed, and random init of vehicle
parameters. However, my confusion comes from the fact that if I run scenario 20
only, N number of times, the behavior is always the same (even though there
should be randomness here as well if I am not mistaken?!).Similarly, when I run
scenario 20 after scenario 10, N number of times, I get the same results all N
times.
Does this make sense?
(Note that I set both, the starting speed and the starting position of the
vehicles, to a specific value, not to randomly chosen one.)
Thanks!
VGBranka
Am Montag, 13. April 2020, 22:36:07 MESZ hat Jakob Erdmann
<[email protected]> Folgendes geschrieben:
The sumo-controlled vehicles are using the default model with stochastic
components. This will result in divergent vehicle behavior when running a
different simulation sequence with a different number / ordering of random
number generator calls. For details, see
https://sumo.dlr.de/docs/Simulation/Randomness.html
regards,Jakob
Am Mo., 13. Apr. 2020 um 15:08 Uhr schrieb Branka Mirchevska
<[email protected]>:
Hello,
I'm trying to run 2 predefined Sumo scenarios one after the other (scenario
ids 10_0, and 20_0).
They contain 10 and 20 Sumo controlled vehicles each, and I place my
remotely controlled vehicle at a certain position in both to drive among
them.
The problem is the following:
when I run scenario 10 and then scenario 20 after it, the behavior is
different than when I run scenario 20 right away.
here is a demonstration:
Only scenario 20_0:
_________________________________
Scenario ID: 20_0
---------------------------------------- time-step: 0
EGO lane pos and speed : 100.0 , 8.0
x, y, angle, speed for EGO to execute: 101.530884567 5.19671627088
90.7360694615 8.10524126657
HUMAN_19 lane position and speed : 121.0 10.0
---------------------------------------- time-step: 1
EGO lane pos and speed : 101.530884567 , 8.10524126657
x, y, angle, speed for EGO to execute: 103.100752622 5.1765673393
91.6889555588 8.39699835768
HUMAN_19 lane position and speed : 123.054754372 10.2737718593
---------------------------------------- time-step: 2
...
First scenario 10_0 and then 20_0:
__________________________________
Scenario ID: 20_0
---------------------------------------- time-step: 0
EGO lane pos and speed : 100.0 , 8.0
x, y, angle, speed for EGO to execute: 101.530884567 5.19671627088
90.7360694615 8.10524126657
HUMAN_19 lane position and speed : 121.0 10.0
---------------------------------------- time-step: 1
EGO lane pos and speed : 101.530884567 , 8.10524126657
x, y, angle, speed for EGO to execute: 103.100752622 5.1765673393
91.6889555588 8.39699835768
HUMAN_19 lane position and speed : 123.093816682 10.4690834098
---------------------------------------- time-step: 2
…
You can see that the behavior of HUMAN_19 is slightly different in time-step
1 event though EGO doesn’t change its remote control parameters.
I have included a small example in the .zip folder attached. If you run:
python2.7 small_example/src/run_scenario.py,
first scenario 10_0 then scenario 20_0 will be executed, one after another.
If you comment in line 63 (#scenario_ids = ['20_0']) in run_example.py, only
scenario 20_0 will be executed.
After running the two options, you will be able to see the above output,
where in time-step 1 ‘HUMAN_19’ behaves differently even though the
environment is exactly the same.
Do you have any clue why?
Thanks!
Best,
Branka
small_example.zip
<http://sumo-user-mailing-list.90755.n8.nabble.com/file/t361/small_example.zip>
--
Sent from: http://sumo-user-mailing-list.90755.n8.nabble.com/
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