97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
# %%
|
|
import sys
|
|
sys.path.append('../../../')
|
|
|
|
from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
|
|
from imitation.algorithms import adversarial
|
|
import stable_baselines3
|
|
import torch.utils.data
|
|
import numpy as np
|
|
from intersim.envs.intersimple import NRasterized, speed_reward
|
|
import itertools
|
|
import functools
|
|
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
|
|
from src.evaluation.evaluation import Evaluation
|
|
|
|
model_name = 'gail_options_image'
|
|
env_settings = {'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2}
|
|
|
|
ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10]] # option 0 is safe fallback
|
|
|
|
def train(expert_data, epochs=20, expert_batch_size=32, generator_steps=1024, discount=0.99):
|
|
env = NRasterized(**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(NRasterized, 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,
|
|
)
|
|
|
|
# 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 epoch in tqdm(range(epochs)):
|
|
train_discriminator(LLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=expert_batch_size)
|
|
train_generator(HLOptions(env, options=ALL_OPTIONS), generator, discriminator, num_samples=generator_steps)
|
|
|
|
eval_env = env # possibly define eval env on different location
|
|
ev = Evaluation(eval_env, n_eval_episodes=100)
|
|
ev.evaluate(epoch, generator, discriminator, expert_data)
|
|
|
|
return generator
|
|
|
|
# %%
|
|
if __name__ == '__main__':
|
|
# %%
|
|
|
|
with open("../../../scratch/etienne/intersimple/data/NormalizedIntersimpleExpertMu.001_NRasterizedInfoAgent51w36h36mppx2.pkl", "rb") as f:
|
|
trajectories = pickle.load(f)
|
|
transitions = rollout.flatten_trajectories(trajectories)
|
|
generator = train(transitions)
|
|
|
|
generator.save(model_name)
|
|
|
|
# %%
|
|
model = stable_baselines3.PPO.load(model_name)
|
|
|
|
env = RenderOptions(NRasterized(**env_settings), options=ALL_OPTIONS)
|
|
|
|
for s in env.sample_ll(model):
|
|
if s['dones']:
|
|
break
|
|
|
|
env.close(filestr='render/'+model_name)
|