Well, actually I wanted to clarify the process of calculating the final score of our submissions.
I guess that you use this reward function to calculate optimality of solution in Round 1, as long as I found this line in run.py script:
print("Reward : ", sum(list(all_rewards.values())))
So, if you still use this approach (and you definitely use it in flatland_2_0_example.py), the score function is calculated incorrectly.
For example, I can order some agents not to move and enter the environment. In this case, they have no impact on total penalty, so the final score reduces (which of course is incorrect) - I can describe this with more details, if you want.
Thus, there is a bug in score calculation, which can be fixed by changing the default rewards function or making anything else.
Sorry for any of misunderstandings.