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