Can not recover the reported result without any modification of the code

[KuoZhong]

The work inspires me a lot. I am trying to run the code and find that the empirical result given by the code is not consistent with that of the last column in D2 on the adversary battle game. In my result, I get 18.39 for 4-4 and 52.21 for 8-8, where there is a huge performance gap. Since I have not modified the code, the problem may be caused by the evaluation settings or something else. I will list the problems that may be important below:

1. how to select the best set of the agents among the k parallel sets? And, is it used to compete with baselines?
2. what is the random seed of your work?
3. could you please offer the evaluation code?

thank you so much.

[footoredo]

1. For the adversarial battle game, we hold K^2 matches, where we let all K red teams compete against all K blue teams. We calculate the average score for each team in their K matches and choose the highest red team and the highest blue team as the best set. And yes, it is used to compete with baselines.
2. Unfortunately, we did not store the seed.
3. We used maddpg_o/experiments/compete.py for evaluation.

As for your result, my guess is that you did not add the --no-wheel option to remove the reward shaping.

[KuoZhong]

Thank you for your advice. With the --no-wheel option, I get the comparable average return of the first two stages for the adversary battle game. But at the last stage where there are 32 agents, I get 144.1929, which is not consistent with your result.

So, could you please tell me the reason for my result?
Should I also add the --no-wheel option into the selection?

Thank you.