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new observation properties for testing of technical limitations '' Debuggings

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Steffen Illium
2021-11-07 18:15:31 +01:00
parent d69cf75c15
commit ab9cde7853
4 changed files with 28 additions and 71 deletions
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45
studies/e_1.py
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@ -96,7 +96,7 @@ def load_model_run_baseline(seed_path, env_to_run):
# retrieve model class
model_cls = next(val for key, val in h.MODEL_MAP.items() if key in seed_path.parent.name)
# Load both agents
model = model_cls.load(seed_path / 'model.zip')
model = model_cls.load(seed_path / 'model.zip', device='cpu')
# Load old env kwargs
with next(seed_path.glob('*.json')).open('r') as f:
env_kwargs = simplejson.load(f)
@ -128,7 +128,7 @@ def load_model_run_study(seed_path, env_to_run, additional_kwargs_dict):
# retrieve model class
model_cls = next(val for key, val in h.MODEL_MAP.items() if key in seed_path.parent.name)
# Load both agents
models = [model_cls.load(seed_path / 'model.zip') for _ in range(n_agents)]
models = [model_cls.load(seed_path / 'model.zip', device='cpu') for _ in range(n_agents)]
# Load old env kwargs
with next(seed_path.glob('*.json')).open('r') as f:
env_kwargs = simplejson.load(f)
@ -179,6 +179,7 @@ if __name__ == '__main__':
# Define properties object parameters
obs_props = ObservationProperties(render_agents=AgentRenderOptions.NOT,
omit_agent_self=True,
additional_agent_placeholder=None,
frames_to_stack=3,
pomdp_r=2
)
@ -202,12 +203,12 @@ if __name__ == '__main__':
# Bundle both environments with global kwargs and parameters
env_map = {'dirt': (DirtFactory, dict(dirt_prop=dirt_props,
**factory_kwargs)),
**factory_kwargs.copy())),
'item': (ItemFactory, dict(item_prop=item_props,
**factory_kwargs)),
**factory_kwargs.copy())),
'itemdirt': (DirtItemFactory, dict(dirt_prop=dirt_props,
item_prop=item_props,
**factory_kwargs))}
**factory_kwargs.copy()))}
env_names = list(env_map.keys())
# Define parameter versions according with #1,2[1,0,N],3
@ -240,6 +241,7 @@ if __name__ == '__main__':
dict(obs_prop=ObservationProperties(
render_agents=AgentRenderOptions.LEVEL,
omit_agent_self=True,
additional_agent_placeholder=None,
frames_to_stack=3,
pomdp_r=2)
)
@ -249,6 +251,7 @@ if __name__ == '__main__':
post_training_kwargs=
dict(obs_prop=ObservationProperties(
render_agents=AgentRenderOptions.NOT,
additional_agent_placeholder=None,
omit_agent_self=True,
frames_to_stack=3,
pomdp_r=2)
@ -259,18 +262,18 @@ if __name__ == '__main__':
# Train starts here ############################################################
# Build Major Loop parameters, parameter versions, Env Classes and models
if True:
for observation_mode in observation_modes.keys():
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']]:
# 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
combination_path = study_root_path / observation_mode / env_name / identifier
combination_path = study_root_path / obs_mode / env_name / identifier
env_class, env_kwargs = env_map[env_name]
env_kwargs = env_kwargs.copy()
# Retrieve and set the observation mode specific env parameters
if observation_mode_kwargs := observation_modes.get(observation_mode, None):
if additional_env_kwargs := observation_mode_kwargs.get("additional_env_kwargs", None):
env_kwargs.update(additional_env_kwargs)
additional_kwargs = observation_modes.get(obs_mode, {}).get("additional_env_kwargs", {})
env_kwargs.update(additional_kwargs)
for seed in range(5):
env_kwargs.update(env_seed=seed)
# Output folder
@ -320,7 +323,10 @@ if __name__ == '__main__':
gc.collect()
# Compare performance runs, for each seed within a model
compare_seed_runs(combination_path, use_tex=False)
try:
compare_seed_runs(combination_path, use_tex=False)
except ValueError:
pass
# Better be save then sorry: Clean up!
try:
del env_kwargs
@ -332,8 +338,11 @@ if __name__ == '__main__':
# Compare performance runs, for each model
# FIXME: Check THIS!!!!
compare_model_runs(study_root_path / observation_mode / env_name, f'{start_time}', 'step_reward',
use_tex=False)
try:
compare_model_runs(study_root_path / obs_mode / env_name, f'{start_time}', 'step_reward',
use_tex=False)
except ValueError:
pass
pass
pass
pass
@ -343,8 +352,8 @@ if __name__ == '__main__':
# Evaluation starts here #####################################################
# First Iterate over every model and monitor "as trained"
if True:
for observation_mode in observation_modes:
obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == observation_mode)
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
for env_path in [x for x in obs_mode_path.iterdir() if x.is_dir()]:
for policy_path in [x for x in env_path.iterdir() if x. is_dir()]:
@ -364,8 +373,8 @@ if __name__ == '__main__':
# Then iterate over every model and monitor "ood behavior" - "is it ood?"
if True:
for observation_mode in observation_modes:
obs_mode_path = next(x for x in study_root_path.iterdir() if x.is_dir() and x.name == observation_mode)
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
for env_path in [x for x in obs_mode_path.iterdir() if x.is_dir()]:
for policy_path in [x for x in env_path.iterdir() if x. is_dir()]:
@ -381,7 +390,7 @@ if __name__ == '__main__':
result = pool.starmap(load_model_run_study,
it.product(paths,
(env_map[env_path.name][0],),
(observation_modes[observation_mode],))
(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)