import sys
from pathlib import Path
from stable_baselines3.common.vec_env import SubprocVecEnv
try:
# noinspection PyUnboundLocalVariable
if __package__ is None:
DIR = Path(__file__).resolve().parent
sys.path.insert(0, str(DIR.parent))
__package__ = DIR.name
else:
DIR = None
except NameError:
DIR = None
pass
import simplejson
from environments.logging.recorder import EnvRecorder
from environments import helpers as h
from environments.factory.factory_dirt import DirtFactory
from environments.factory.dirt_util import DirtProperties
from environments.factory.factory_item import ItemFactory
from environments.factory.additional.item.item_util import ItemProperties
from environments.logging.envmonitor import EnvMonitor
from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
"""
In this studie, we want to export trained Agents for debugging purposes.
"""
def encapsule_env_factory(env_fctry, env_kwrgs):
def _init():
with env_fctry(**env_kwrgs) as init_env:
return init_env
return _init
def load_model_run_baseline(policy_path, env_to_run):
# retrieve model class
model_cls = h.MODEL_MAP['A2C']
# Load both agent
model = model_cls.load(policy_path / 'model.zip', device='cpu')
# Load old env kwargs
with next(policy_path.glob('*params.json')).open('r') as f:
env_kwargs = simplejson.load(f)
env_kwargs.update(done_at_collision=True)
# Init Env
with env_to_run(**env_kwargs) as env_factory:
monitored_env_factory = EnvMonitor(env_factory)
recorded_env_factory = EnvRecorder(monitored_env_factory)
# Evaluation Loop for i in range(n Episodes)
for episode in range(5):
env_state = recorded_env_factory.reset()
rew, done_bool = 0, False
while not done_bool:
action = model.predict(env_state, deterministic=True)[0]
env_state, step_r, done_bool, info_obj = recorded_env_factory.step(action)
rew += step_r
if done_bool:
break
print(f'Factory run {episode} done, reward is:\n {rew}')
recorded_env_factory.save_run(filepath=policy_path / f'monitor.pick')
recorded_env_factory.save_records(filepath=policy_path / f'recorder.json')
if __name__ == '__main__':
# What to do:
train = True
individual_run = True
combined_run = False
multi_env = False
train_steps = 2e6
frames_to_stack = 3
# Define a global studi save path
study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}'
def policy_model_kwargs():
return dict()
# Define Global Env Parameters
# Define properties object parameters
obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT,
additional_agent_placeholder=None,
omit_agent_self=True,
frames_to_stack=frames_to_stack,
pomdp_r=2, cast_shadows=True)
move_props = MovementProperties(allow_diagonal_movement=True,
allow_square_movement=True,
allow_no_op=False)
dirt_props = DirtProperties(initial_dirt_ratio=0.35, initial_dirt_spawn_r_var=0.1,
clean_amount=0.34,
max_spawn_amount=0.1, max_global_amount=20,
max_local_amount=1, spawn_frequency=0, max_spawn_ratio=0.05,
dirt_smear_amount=0.0, agent_can_interact=True)
item_props = ItemProperties(n_items=10, spawn_frequency=30, n_drop_off_locations=2,
max_agent_inventory_capacity=15)
factory_kwargs = dict(n_agents=1, max_steps=500, parse_doors=True,
level_name='rooms', doors_have_area=True,
verbose=False,
mv_prop=move_props,
obs_prop=obs_props,
done_at_collision=False
)
# Bundle both environments with global kwargs and parameters
env_map = {}
env_map.update({'dirt': (DirtFactory, dict(dirt_prop=dirt_props,
**factory_kwargs.copy()))})
env_map.update({'item': (ItemFactory, dict(item_prop=item_props,
**factory_kwargs.copy()))})
# env_map.update({'dest': (DestFactory, dict(dest_prop=dest_props,
# **factory_kwargs.copy()))})
env_names = list(env_map.keys())
# Train starts here ############################################################
# Build Major Loop parameters, parameter versions, Env Classes and models
if train:
for env_key in (env_key for env_key in env_map if 'combined' != env_key):
model_cls = h.MODEL_MAP['A2C']
combination_path = study_root_path / env_key
env_class, env_kwargs = env_map[env_key]
# Output folder
if (combination_path / 'monitor.pick').exists():
continue
combination_path.mkdir(parents=True, exist_ok=True)
if not multi_env:
env_factory = encapsule_env_factory(env_class, env_kwargs)()
else:
env_factory = SubprocVecEnv([encapsule_env_factory(env_class, env_kwargs)
for _ in range(6)], start_method="spawn")
param_path = combination_path / f'env_params.json'
try:
env_factory.env_method('save_params', param_path)
except AttributeError:
env_factory.save_params(param_path)
# EnvMonitor Init
callbacks = [EnvMonitor(env_factory)]
# Model Init
model = model_cls("MlpPolicy", env_factory, **policy_model_kwargs(),
verbose=1, seed=69, device='cpu')
# Model train
model.learn(total_timesteps=int(train_steps), callback=callbacks)
# Model save
save_path = combination_path / f'model.zip'
model.save(save_path)
# Monitor Save
callbacks[0].save_run(combination_path / 'monitor.pick')
# Better be save then sorry: Clean up!
del env_factory, model
import gc
gc.collect()
# Train ends here ############################################################
# Evaluation starts here #####################################################
# First Iterate over every model and monitor "as trained"
if individual_run:
print('Start Individual Recording')
for env_key in (env_key for env_key in env_map if 'combined' != env_key):
# For trained policy in study_root_path / _identifier
policy_path = study_root_path / env_key
load_model_run_baseline(policy_path, env_map[policy_path.name][0])
# for policy_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_baseline(policy_path)
print('Done Individual Recording')