Comments (5)
MultiTaskSampler
, which is responsible for sampling the trajectories, is doing adaptation locally in each worker.
pytorch-maml-rl/maml_rl/samplers/multi_task_sampler.py
Lines 251 to 275 in 0c2c7dd
So in
test.py
, you do get both trajectories before and after adaptation with the simple call to MultiTaskSampler
. And with a few changes to test.py
you can even use different number of gradient steps for adaptation by changing num_steps
in your call to sampler.sample()
.from pytorch-maml-rl.
Thanks, That was really helpful.
from pytorch-maml-rl.
Sorry for opening this issue again but after changing num_steps I didn't get better results!!
(number near to MAML show num-batches)
from pytorch-maml-rl.
What is the environment? Making sure you get better performance with a larger number of gradient steps at test time is not something I tested.
from pytorch-maml-rl.
Sorry for bothering you. It was my mistake. I found out if I lower the learning rate at both test and train time I can get better performance. (my environment is half_cheetah_vel)
from pytorch-maml-rl.
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