Selecting seeds during training

Hi!

How are you enforcing the usage of 200 training seeds once submitted?
I’m planning on a submission that has some logics to sample certain seeds for each environment.
And as far as Procgen is implemented, I’d have to close and instantiate again the environment to apply the designated seed.

Any info about this @mohanty ?

Hi @Leckofunny,

So in the warm-up round and round-1, we will not be evaluating generalization. So during the training phase your code will have access to all the levels.

In round-2, we will be restricting access to only 200 levels during the training phase by enforcing num_levels=200 during all env instantiation.

Does this answer your question ?.

Cheers,
Mohanty

@mohanty
I’d like to explicitly set a distinct seed for each worker during training, because I’ve got a concept for sampling seeds.
The implementation would probably look similar to this:
https://docs.ray.io/en/master/rllib-training.html#curriculum-learning

As far as I know, the Procgen environment has to be closed and instantiated again to apply a distinct seed (num_leves = 1, start_level = my_desired_seed), because I cannot enforce a new seed during the reset() call.

So I assume that 200 seeds will be sampled uniformly and it will not be possible to inject my logic to alter the sampling strategy of the 200 seeds.