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Debugging

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Steffen Illium
2022-01-11 13:45:00 +01:00
parent 3150757347
commit b6c8cbd2e3
5 changed files with 70 additions and 47 deletions
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26
studies/single_run_with_export.py
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@ -114,6 +114,7 @@ if __name__ == '__main__':
train = True
individual_run = True
combined_run = True
multi_env = False
train_steps = 2e5
frames_to_stack = 3
@ -122,7 +123,7 @@ if __name__ == '__main__':
study_root_path = Path(__file__).parent.parent / 'study_out' / f'{Path(__file__).stem}'
def policy_model_kwargs():
return dict(learning_rate=0.0003, n_steps=10, gamma=0.95, gae_lambda=0.0, ent_coef=0.01, vf_coef=0.5)
return dict()
# Define Global Env Parameters
# Define properties object parameters
@ -142,22 +143,22 @@ if __name__ == '__main__':
item_props = ItemProperties(n_items=10, spawn_frequency=30, n_drop_off_locations=2,
max_agent_inventory_capacity=15)
dest_props = DestProperties(n_dests=4, spawn_mode=DestModeOptions.GROUPED, spawn_frequency=1)
factory_kwargs = dict(n_agents=1, max_steps=400, parse_doors=True,
level_name='rooms', doors_have_area=False,
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=True
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_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_map.update({'combined': (DirtDestItemFactory, dict(dest_prop=dest_props,
item_prop=item_props,
dirt_prop=dirt_props,
@ -168,7 +169,7 @@ if __name__ == '__main__':
# 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']
model_cls = h.MODEL_MAP['PPO']
combination_path = study_root_path / env_key
env_class, env_kwargs = env_map[env_key]
@ -177,8 +178,11 @@ if __name__ == '__main__':
continue
combination_path.mkdir(parents=True, exist_ok=True)
env_factory = SubprocVecEnv([encapsule_env_factory(env_class, env_kwargs)
for _ in range(6)], start_method="spawn")
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: