101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
# %%
|
|
import sys
|
|
sys.path.append('../../../')
|
|
|
|
from src.discriminator import CnnDiscriminatorFlatAction
|
|
from imitation.algorithms import adversarial
|
|
import stable_baselines3
|
|
import torch.utils.data
|
|
import numpy as np
|
|
from intersim.envs import NRasterizedRouteSpeedRandomAgentLocation
|
|
import itertools
|
|
from torch.distributions import Categorical
|
|
import gym
|
|
import torch
|
|
import pickle
|
|
import imitation.data.rollout as rollout
|
|
import tempfile
|
|
import pathlib
|
|
from imitation.util import logger
|
|
from stable_baselines3.common.env_util import make_vec_env
|
|
from tqdm import tqdm
|
|
from src.policies.options import OptionsCnnPolicy
|
|
from src.gail.options import OptionsEnv, LLOptions, HLOptions, RenderOptions
|
|
from src.gail.train import train_discriminator, train_generator
|
|
|
|
model_name = 'gail_options_image_random_location'
|
|
env_settings = {'width': 70, 'height': 70, 'm_per_px': 1, 'map_color': 128, 'mu': 0.001}
|
|
|
|
ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
|
|
|
|
def train(
|
|
expert_data,
|
|
epochs=200,
|
|
expert_batch_size=1024,
|
|
generator_steps=1024,
|
|
discount=0.99,
|
|
n_disc_updates_per_round=10,
|
|
n_gen_updates_per_round=10,
|
|
):
|
|
env = NRasterizedRouteSpeedRandomAgentLocation(**env_settings)
|
|
env.discount = discount
|
|
|
|
tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
|
|
tempdir_path = pathlib.Path(tempdir.name)
|
|
logger.configure(tempdir_path / "GAIL/")
|
|
print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
|
|
|
|
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,
|
|
discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
|
|
#discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
|
|
venv=venv, # unused
|
|
gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
|
|
)
|
|
|
|
generator = stable_baselines3.PPO(
|
|
OptionsCnnPolicy,
|
|
OptionsEnv(env, options=ALL_OPTIONS),
|
|
verbose=1,
|
|
n_steps=generator_steps,
|
|
n_epochs=n_gen_updates_per_round,
|
|
)
|
|
|
|
# PPO.train requires logger as set up in
|
|
# PPO._setup_learn (called by PPO.learn)
|
|
generator._logger = stable_baselines3.common.utils.configure_logger(
|
|
generator.verbose,
|
|
generator.tensorboard_log,
|
|
)
|
|
|
|
for _ in tqdm(range(epochs)):
|
|
train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size, n_updates=n_disc_updates_per_round)
|
|
train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
|
|
generator.save(model_name)
|
|
|
|
return generator
|
|
|
|
def video(model_name, env):
|
|
model = stable_baselines3.PPO.load(model_name)
|
|
env = RenderOptions(env, options=ALL_OPTIONS)
|
|
for s in env.sample_ll(model):
|
|
if s['dones']:
|
|
break
|
|
env.close(filestr='render/'+model_name)
|
|
|
|
def evaluate():
|
|
video(
|
|
model_name=model_name,
|
|
env=NRasterizedRouteSpeedRandomAgentLocation(**env_settings)
|
|
)
|
|
|
|
# %%
|
|
if __name__ == '__main__':
|
|
|
|
with open("data/NormalizedIntersimpleExpertMu.001N10000_NRasterizedRouteSpeedRandomAgentLocationw70h70mppx1mapc128mu.001.pkl", "rb") as f:
|
|
trajectories = pickle.load(f)
|
|
transitions = rollout.flatten_trajectories(trajectories)
|
|
train(transitions)
|