139 lines
4.2 KiB
Python
139 lines
4.2 KiB
Python
from intersim.envs.intersimple import Intersimple, InfoFilter
|
|
from stable_baselines3.common.policies import BasePolicy
|
|
import gym
|
|
from intersim.envs.intersimple import *
|
|
import imitation.data.rollout as rollout
|
|
from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
|
|
from imitation.data.wrappers import RolloutInfoWrapper
|
|
from gail.envs import NRasterizedRouteSpeedRandomAgentLocation
|
|
|
|
class IntersimExpert(BasePolicy):
|
|
|
|
def __init__(self, intersim_env, mu=0, *args, **kwargs):
|
|
super().__init__(
|
|
observation_space=gym.spaces.Space(),
|
|
action_space=gym.spaces.Space(),
|
|
*args, **kwargs
|
|
)
|
|
self._intersim = intersim_env
|
|
self._mu = mu
|
|
|
|
def forward(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def _predict(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def _action(self):
|
|
target_t = min(self._intersim._ind + 1, len(self._intersim._svt.simstate) - 1)
|
|
target_state = self._intersim._svt.simstate[target_t]
|
|
return self._intersim.target_state(target_state, mu=self._mu)
|
|
|
|
def predict(self, *args, **kwargs):
|
|
return self._action(), None
|
|
|
|
class IntersimpleExpert(BasePolicy):
|
|
|
|
def __init__(self, intersimple_env, mu=0, *args, **kwargs):
|
|
super().__init__(
|
|
observation_space=intersimple_env.observation_space,
|
|
action_space=intersimple_env.action_space,
|
|
*args, **kwargs
|
|
)
|
|
self._intersimple = intersimple_env
|
|
self._intersim_expert = IntersimExpert(intersimple_env._env, mu=mu)
|
|
|
|
def forward(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def _predict(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def _action(self):
|
|
# RandomLocation mixin re-initializes the intersim sub-env
|
|
self._intersim_expert._intersim = self._intersimple._env
|
|
return self._intersim_expert._action()[self._intersimple._agent]
|
|
|
|
def predict(self, *args, **kwargs):
|
|
return self._action(), None
|
|
|
|
class NormalizedIntersimpleExpert(IntersimpleExpert):
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def predict(self, *args, **kwargs):
|
|
action, _ = super().predict(*args, **kwargs)
|
|
return self._intersimple._normalize(action), None
|
|
|
|
class DummyVecEnvPolicy(BasePolicy):
|
|
|
|
def __init__(self, experts):
|
|
self._experts = [e() for e in experts]
|
|
|
|
def forward(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def _predict(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def predict(self, *args, **kwargs):
|
|
predictions = [e.predict() for e in self._experts]
|
|
actions = [p[0] for p in predictions]
|
|
states = [p[1] for p in predictions]
|
|
return actions, states
|
|
|
|
def forward(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def _predict(self, *args, **kwargs):
|
|
raise NotImplementedError()
|
|
|
|
def save_video(env, expert):
|
|
env.reset()
|
|
env.render()
|
|
done = False
|
|
while not done:
|
|
actions, _ = expert.predict()
|
|
_, _, done, _ = env.step(actions)
|
|
env.render()
|
|
env.close()
|
|
|
|
def demonstrations(expert='NormalizedIntersimpleExpert', env='NRasterizedRandomAgent', path=None, min_timesteps=25000, min_episodes=None, video=False, env_args={}, policy_args={}):
|
|
"""Rollout and save expert demos.
|
|
|
|
Usage:
|
|
python -m intersimple.expert <flags>
|
|
|
|
"""
|
|
Env = globals()[env]
|
|
Expert = globals()[expert]
|
|
|
|
env = Env(**env_args)
|
|
info_env = RolloutInfoWrapper(env)
|
|
venv = DummyVecEnv([lambda: info_env])
|
|
|
|
policy = Expert(env, **policy_args)
|
|
venv_policy = DummyVecEnvPolicy([lambda: policy])
|
|
|
|
if video:
|
|
save_video(env, policy)
|
|
|
|
path = path or (policy.__class__.__name__ + '_' + env.__class__.__name__ + '.pkl')
|
|
include_infos = isinstance(env, InfoFilter)
|
|
|
|
rollout.rollout_and_save(
|
|
path=path,
|
|
policy=venv_policy,
|
|
venv=venv,
|
|
sample_until=rollout.make_sample_until(
|
|
min_timesteps=min_timesteps,
|
|
min_episodes=min_episodes,
|
|
),
|
|
exclude_infos=not include_infos,
|
|
)
|
|
|
|
if __name__ == '__main__':
|
|
import fire
|
|
fire.Fire(demonstrations)
|