Closed prinshul closed 7 months ago
Hey! Thanks for reaching out.
So the steps you see in wandb above are training iterations.
The ones below are collected frames
every training iteration you collect on_policy_frames_per_batch
so if you multiply the x axis of the plot above by that, you get the plot below.
Otherwise in wandb you can also change the x axis to counters/total_frames
to obtain the plot below
Lemme know if this helps
The fact that it stops at 10 million frames is determined by this param in the config https://github.com/facebookresearch/BenchMARL/blob/02fc1b68f173106934d86824b9ad865ef21a46db/fine_tuned/vmas/conf/config.yaml#L38
Got it. Thank you.
Also, the plot is slightly different from the one given by you for tuned MAPPO + Balance https://api.wandb.ai/links/matteobettini/r5744vas
I got this:
Although the mean reward is almost similar (but not the same though I just ran it without any change). Is this due to randomness during multiple runs? Is this because of seed not set?
Getting the exact same results in 2 different machines with 2 different python environments is basically impossible even if we set the same seed.
The thing you can check (and that should be true) is that if you run the script a second time, with the same seed and same setup on your machine, you get the exact same results.
That said, those 2 curves look really similar so that is good news!
Sure. Thanks.
Hi,
How timesteps are controlled for vmas? I ran balance env with mappo with tuned hyperparameters. In wandb it shows 165 steps. But when I plotted with marl-eval I can see 1e7 timesteps.
![bal](https://github.com/facebookresearch/BenchMARL/assets/13760669/cd94e602-6631-4490-b12b-2385cfc0a70d)
Also, the plot is slightly different from the one given for tuned MAPPO + Balance.