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Use rasterized speed

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This commit is contained in:
ebuehrle
2021-10-29 15:58:34 +02:00
parent 8dd42abbf3
commit 2e8c7f1ace
2 changed files with 7 additions and 6 deletions
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3
scratch/etienne/intersimple/data/generate.sh
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@@ -8,4 +8,5 @@
#python -m expert --env=NRasterizedRandomAgent --min_timesteps=10000 --env_args='{width:36,height:36,m_per_px:2}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRandomAgentw36h36mppx2.pkl'
#python -m expert --env=NRasterizedRouteRandomAgent --min_timesteps=10000 --env_args='{width:70,height:70,m_per_px:1}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteRandomAgentw70h70mppx1.pkl'
#python -m expert --env=NRasterizedRouteRandomAgentLocation --min_timesteps=100000 --env_args='{width:70,height:70,m_per_px:1}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N100000_NRasterizedRouteRandomAgentLocationw70h70mppx1.pkl'
python -m expert --env=NRasterizedRouteRandomAgentLocation --min_timesteps=100000 --env_args='{width:70,height:70,m_per_px:1,map_color:128}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N100000_NRasterizedRouteRandomAgentLocationw70h70mppx1mapc128.pkl'
#python -m expert --env=NRasterizedRouteRandomAgentLocation --min_timesteps=100000 --env_args='{width:70,height:70,m_per_px:1,map_color:128}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N100000_NRasterizedRouteRandomAgentLocationw70h70mppx1mapc128.pkl'
python -m expert --env=NRasterizedRouteSpeedRandomAgentLocation --min_timesteps=10000 --env_args='{width:70,height:70,m_per_px:1,map_color:128,mu:0.001}' --policy_args='{mu:0.001}' --path='NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteSpeedRandomAgentLocationw70h70mppx1mapc128mu.001.pkl'

10
scratch/etienne/intersimple/gail_options_image_random_location.py
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@@ -7,7 +7,7 @@ from imitation.algorithms import adversarial
import stable_baselines3
import torch.utils.data
import numpy as np
from intersim.envs import NRasterizedRouteRandomAgentLocation
from intersim.envs import NRasterizedRouteSpeedRandomAgentLocation
import itertools
from torch.distributions import Categorical
import gym
@@ -37,7 +37,7 @@ def train(
n_disc_updates_per_round=2,
n_gen_updates_per_round=10,
):
env = NRasterizedRouteRandomAgentLocation(**env_settings)
env = NRasterizedRouteSpeedRandomAgentLocation(**env_settings)
env.discount = discount
tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
@@ -45,7 +45,7 @@ def train(
logger.configure(tempdir_path / "GAIL/")
print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
venv = make_vec_env(NRasterizedRouteRandomAgentLocation, n_envs=1, env_kwargs=env_settings)
venv = make_vec_env(NRasterizedRouteSpeedRandomAgentLocation, n_envs=1, env_kwargs=env_settings)
discriminator = adversarial.GAIL(
expert_data=expert_data,
expert_batch_size=expert_batch_size,
@@ -88,13 +88,13 @@ def video(model_name, env):
def evaluate():
video(
model_name=model_name,
env=NRasterizedRouteRandomAgentLocation(**env_settings)
env=NRasterizedRouteSpeedRandomAgentLocation(**env_settings)
)
# %%
if __name__ == '__main__':
with open("data/NormalizedIntersimpleExpertMu.001N100000_NRasterizedRouteRandomAgentLocationw70h70mppx1mapc128.pkl", "rb") as f:
with open("data/NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteSpeedRandomAgentLocationw70h70mppx1mapc128mu.001.pkl", "rb") as f:
trajectories = pickle.load(f)
transitions = rollout.flatten_trajectories(trajectories)
train(transitions)