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Debugging and collision rendering

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This commit is contained in:
Steffen Illium 2021-11-11 10:58:11 +01:00
parent ab9cde7853
commit 6287380f60
5 changed files with 117 additions and 67 deletions
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4
environments/factory/base/base_factory.py
View File

@ -494,10 +494,10 @@ class BaseFactory(gym.Env):
if self._actions.is_moving_action(agent.temp_action):
if agent.temp_valid:
# info_dict.update(movement=1)
reward -= 0.001
reward -= 0.01
pass
else:
reward -= 0.01
reward -= 0.05
self.print(f'{agent.name} just hit the wall at {agent.pos}.')
per_agent_info_dict[agent.name].update({f'{agent.name}_vs_LEVEL': 1})

2
environments/factory/factory_dirt.py
View File

@ -65,7 +65,7 @@ class DirtRegister(MovingEntityObjectRegister):
def as_array(self):
if self._array is not None:
self._array[:] = c.FREE_CELL.value
for dirt in self.values():
for dirt in list(self.values()):
if dirt.amount == 0:
self.delete_item(dirt)
self._array[0, dirt.x, dirt.y] = dirt.amount

2
environments/helpers.py
View File

@ -136,7 +136,7 @@ def asset_str(agent):
# if any([x is None for x in [self._slices[j] for j in agent.collisions]]):
# print('error')
col_names = [x.name for x in agent.temp_collisions]
if c.AGENT.value in col_names:
if any(c.AGENT.value in name for name in col_names):
return 'agent_collision', 'blank'
elif not agent.temp_valid or c.LEVEL.name in col_names or c.AGENT.name in col_names:
return c.AGENT.value, 'invalid'

17
reload_agent.py
View File

@ -16,11 +16,11 @@ warnings.filterwarnings('ignore', category=UserWarning)
if __name__ == '__main__':
model_name = 'DQN_163519000'
model_name = 'A2C_ItsDirt'
run_id = 0
seed = 69
n_agents = 2
out_path = Path('debug_out/DQN_163519000/1_DQN_163519000')
seed = 67
n_agents = 1
out_path = Path('study_out/e_1_ItsDirt/no_obs/dirt/A2C_ItsDirt/0_A2C_ItsDirt')
model_path = out_path
with (out_path / f'env_params.json').open('r') as f:
@ -46,10 +46,17 @@ if __name__ == '__main__':
env_state = env.reset()
rew, done_bool = 0, False
while not done_bool:
if n_agents > 1:
actions = [model.predict(
np.stack([env_state[i][j] for i in range(env_state.shape[0])]),
deterministic=False)[0] for j, model in enumerate(models)]
deterministic=True)[0] for j, model in enumerate(models)]
else:
actions = models[0].predict(env_state, deterministic=True)[0]
if any([agent.pos in [door.pos for door in env.unwrapped[c.DOORS]]
for agent in env.unwrapped[c.AGENT]]):
print('On Door')
env_state, step_r, done_bool, info_obj = env.step(actions)
recorder.read_info(0, info_obj)
rew += step_r
env.render()

103
studies/e_1.py
View File

@ -147,7 +147,7 @@ def load_model_run_study(seed_path, env_to_run, additional_kwargs_dict):
try:
actions = [model.predict(
np.stack([env_state[i][j] for i in range(env_state.shape[0])]),
deterministic=False)[0] for j, model in enumerate(models)]
deterministic=True)[0] for j, model in enumerate(models)]
except ValueError as e:
print(e)
print('Env_Kwargs are:\n')
@ -169,10 +169,11 @@ def load_model_run_study(seed_path, env_to_run, additional_kwargs_dict):
if __name__ == '__main__':
train_steps = 8e5
train_steps = 5e6
n_seeds = 3
# Define a global studi save path
start_time = '900000' # int(time.time())
start_time = 'Now_with_doors' # int(time.time())
study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}_{start_time}'
# Define Global Env Parameters
@ -195,29 +196,65 @@ if __name__ == '__main__':
spawn_frequency=30, n_drop_off_locations=2,
max_agent_inventory_capacity=15)
factory_kwargs = dict(n_agents=1, max_steps=400, parse_doors=True,
level_name='rooms', record_episodes=False, doors_have_area=False,
level_name='rooms', record_episodes=False, doors_have_area=True,
verbose=False,
mv_prop=move_props,
obs_prop=obs_props
)
# Bundle both environments with global kwargs and parameters
env_map = {'dirt': (DirtFactory, dict(dirt_prop=dirt_props,
**factory_kwargs.copy())),
'item': (ItemFactory, dict(item_prop=item_props,
**factory_kwargs.copy())),
'itemdirt': (DirtItemFactory, dict(dirt_prop=dirt_props,
item_prop=item_props,
**factory_kwargs.copy()))}
env_map = {}
env_map.update({'dirt': (DirtFactory, dict(dirt_prop=dirt_props,
**factory_kwargs.copy()))})
if False:
env_map.update({'item': (ItemFactory, dict(item_prop=item_props,
**factory_kwargs.copy()))})
env_map.update({'itemdirt': (DirtItemFactory, dict(dirt_prop=dirt_props, item_prop=item_props,
**factory_kwargs.copy()))})
env_names = list(env_map.keys())
# Define parameter versions according with #1,2[1,0,N],3
observation_modes = {
# Fill-value = 0
# DEACTIVATED 'seperate_0': dict(additional_env_kwargs=dict(additional_agent_placeholder=0)),
# Fill-value = 1
# DEACTIVATED 'seperate_1': dict(additional_env_kwargs=dict(additional_agent_placeholder=1)),
# Fill-value = N(0, 1)
observation_modes = {}
if False:
observation_modes.update({
'seperate_1': dict(
post_training_kwargs=
dict(obs_prop=ObservationProperties(
render_agents=AgentRenderOptions.COMBINED,
additional_agent_placeholder=None,
omit_agent_self=True,
frames_to_stack=3,
pomdp_r=2)
),
additional_env_kwargs=
dict(obs_prop=ObservationProperties(
render_agents=AgentRenderOptions.NOT,
additional_agent_placeholder=1,
omit_agent_self=True,
frames_to_stack=3,
pomdp_r=2)
)
)})
observation_modes.update({
'seperate_0': dict(
post_training_kwargs=
dict(obs_prop=ObservationProperties(
render_agents=AgentRenderOptions.COMBINED,
additional_agent_placeholder=None,
omit_agent_self=True,
frames_to_stack=3,
pomdp_r=2)
),
additional_env_kwargs=
dict(obs_prop=ObservationProperties(
render_agents=AgentRenderOptions.NOT,
additional_agent_placeholder=0,
omit_agent_self=True,
frames_to_stack=3,
pomdp_r=2)
)
)})
observation_modes.update({
'seperate_N': dict(
post_training_kwargs=
dict(obs_prop=ObservationProperties(
@ -235,7 +272,8 @@ if __name__ == '__main__':
frames_to_stack=3,
pomdp_r=2)
)
),
)})
observation_modes.update({
'in_lvl_obs': dict(
post_training_kwargs=
dict(obs_prop=ObservationProperties(
@ -245,7 +283,8 @@ if __name__ == '__main__':
frames_to_stack=3,
pomdp_r=2)
)
),
)})
observation_modes.update({
# No further adjustment needed
'no_obs': dict(
post_training_kwargs=
@ -257,14 +296,14 @@ if __name__ == '__main__':
pomdp_r=2)
)
)
}
})
# Train starts here ############################################################
# Build Major Loop parameters, parameter versions, Env Classes and models
if True:
for obs_mode in observation_modes.keys():
for env_name in env_names:
for model_cls in [h.MODEL_MAP['A2C'], h.MODEL_MAP['DQN']]:
for model_cls in [h.MODEL_MAP['A2C']]:
# Create an identifier, which is unique for every combination and easy to read in filesystem
identifier = f'{model_cls.__name__}_{start_time}'
# Train each combination per seed
@ -274,7 +313,7 @@ if __name__ == '__main__':
# Retrieve and set the observation mode specific env parameters
additional_kwargs = observation_modes.get(obs_mode, {}).get("additional_env_kwargs", {})
env_kwargs.update(additional_kwargs)
for seed in range(5):
for seed in range(n_seeds):
env_kwargs.update(env_seed=seed)
# Output folder
seed_path = combination_path / f'{str(seed)}_{identifier}'
@ -352,6 +391,7 @@ if __name__ == '__main__':
# Evaluation starts here #####################################################
# First Iterate over every model and monitor "as trained"
if True:
print('Start Baseline Tracking')
for obs_mode in observation_modes:
obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == obs_mode)
# For trained policy in study_root_path / identifier
@ -370,9 +410,11 @@ if __name__ == '__main__':
# for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_baseline(seed_path)
print('Baseline Tracking done')
# Then iterate over every model and monitor "ood behavior" - "is it ood?"
if True:
print('Start OOD Tracking')
for obs_mode in observation_modes:
obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == obs_mode)
# For trained policy in study_root_path / identifier
@ -387,18 +429,19 @@ if __name__ == '__main__':
pool = mp.Pool(mp.cpu_count())
paths = list(y for y in policy_path.iterdir() if y.is_dir() \
and not (y / ood_monitor_file).exists())
result = pool.starmap(load_model_run_study,
it.product(paths,
(env_map[env_path.name][0],),
(observation_modes[obs_mode],))
)
# for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
# load_model_run_study(seed_path)
# result = pool.starmap(load_model_run_study,
# it.product(paths,
# (env_map[env_path.name][0],),
# (observation_modes[obs_mode],))
# )
for seed_path in (y for y in policy_path.iterdir() if y.is_dir()):
load_model_run_study(seed_path, env_map[env_path.name][0], observation_modes[obs_mode])
print('OOD Tracking Done')
# Plotting
if True:
# TODO: Plotting
print('Start Plotting')
for observation_folder in (x for x in study_root_path.iterdir() if x.is_dir()):
df_list = list()
for env_folder in (x for x in observation_folder.iterdir() if x.is_dir()):