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fixing flataction discriminator to convert to float beforehand, adding necessary forward calls in expert, adding Fire to video creator from model, and trying full run of options gail with new discrimination model

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Arec
2021-10-11 08:47:18 -07:00
parent 01752fac12
commit 70e55327dc
4 changed files with 37 additions and 17 deletions
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8
scratch/etienne/intersimple/gail_options_image.py
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@@ -1,5 +1,5 @@
# %%
from gail.discriminator import CnnDiscriminator
from gail.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
from imitation.algorithms import adversarial
import stable_baselines3
import torch.utils.data
@@ -271,7 +271,8 @@ def train(expert_data, epochs=10, expert_batch_size=32, generator_steps=2048, di
discriminator = adversarial.GAIL(
expert_data=expert_data,
expert_batch_size=expert_batch_size,
discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
#discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
venv=venv, # unused
gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
)
@@ -299,10 +300,11 @@ def train(expert_data, epochs=10, expert_batch_size=32, generator_steps=2048, di
# %%
if __name__ == '__main__':
# %%
with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
trajectories = pickle.load(f)
transitions = rollout.flatten_trajectories(trajectories)
generator = train(transitions, epochs=2, expert_batch_size=2, generator_steps=2)
generator = train(transitions)
generator.save(model_name)