def __init__(self, trainer_config_path, model_path):
self.brain = BrainParameters(
brain_name='Learner',
camera_resolutions=[{
'height': 84,
'width': 84,
'blackAndWhite': False
}],
num_stacked_vector_observations=1,
vector_action_descriptions=['', ''],
vector_action_space_size=[3, 3],
vector_action_space_type=0, # corresponds to discrete
vector_observation_space_size=3)
self.trainer_params = yaml.load(open(trainer_config_path))['Learner']
self.trainer_params['keep_checkpoints'] = 0
self.trainer_params['model_path'] = model_path
self.trainer_params['use_recurrent'] = False
self.policy = PPOPolicy(brain=self.brain,
seed=0,
trainer_params=self.trainer_params,
is_training=False,
load=True)
def __init__(self):
"""
Load your agent here and initialize anything needed
"""
# Load the configuration and model using ABSOLUTE PATHS
self.configuration_file = '/aaio/data/trainer_config.yaml'
self.model_path = '/aaio/data/1-Food/Learner'
self.brain = BrainParameters(
brain_name='Learner',
camera_resolutions=[{
'height': 84,
'width': 84,
'blackAndWhite': False
}],
num_stacked_vector_observations=1,
vector_action_descriptions=['', ''],
vector_action_space_size=[3, 3],
vector_action_space_type=0, # corresponds to discrete
vector_observation_space_size=3)
self.trainer_params = yaml.load(open(
self.configuration_file))['Learner']
self.trainer_params['keep_checkpoints'] = 0
self.trainer_params['model_path'] = self.model_path
self.trainer_params['use_recurrent'] = False
self.policy = PPOPolicy(brain=self.brain,
seed=0,
trainer_params=self.trainer_params,
is_training=False,
load=True)
def __init__(self):
"""
Load your agent here and initialize anything needed
"""
# Load the configuration and model using ABSOLUTE PATHS
self.configuration_file = '/aaio/AnimalAI-Olympics/examples/configs/trainer_config.yaml'
self.model_path = '/aaio/AnimalAI-Olympics/examples/models/train_example/Learner'
self.brain = BrainParameters(brain_name='Learner',
camera_resolutions=[{'height': 84, 'width': 84, 'blackAndWhite': False}],
num_stacked_vector_observations=1,
vector_action_descriptions=['', ''],
vector_action_space_size=[3, 3],
vector_action_space_type=0, # corresponds to discrete
vector_observation_space_size=3
)
self.trainer_params = yaml.load(open(self.configuration_file))['Learner']
self.trainer_params['keep_checkpoints'] = 0
self.trainer_params['model_path'] = self.model_path
self.trainer_params['use_recurrent'] = False
self.policy = PPOPolicy(brain=self.brain,
seed=0,
trainer_params=self.trainer_params,
is_training=False,
load=True)
self.memory = np.zeros( (15, 84, 84, 3) ) # keep 15 frames for prediction
self.internal_model = load_model("model.h5")
def load_demonstration(file_path):
"""
Loads and parses a demonstration file.
:param file_path: Location of demonstration file (.demo).
:return: BrainParameter and list of BrainInfos containing demonstration data.
"""
# First 32 bytes of file dedicated to meta-data.
INITIAL_POS = 33
if not os.path.isfile(file_path):
raise FileNotFoundError(
"The demonstration file {} does not exist.".format(file_path))
file_extension = pathlib.Path(file_path).suffix
if file_extension != '.demo':
raise ValueError(
"The file is not a '.demo' file. Please provide a file with the "
"correct extension.")
brain_params = None
brain_infos = []
data = open(file_path, "rb").read()
next_pos, pos, obs_decoded = 0, 0, 0
total_expected = 0
while pos < len(data):
next_pos, pos = _DecodeVarint32(data, pos)
if obs_decoded == 0:
meta_data_proto = DemonstrationMetaProto()
meta_data_proto.ParseFromString(data[pos:pos + next_pos])
total_expected = meta_data_proto.number_steps
pos = INITIAL_POS
if obs_decoded == 1:
brain_param_proto = BrainParametersProto()
brain_param_proto.ParseFromString(data[pos:pos + next_pos])
brain_params = BrainParameters.from_proto(brain_param_proto)
pos += next_pos
if obs_decoded > 1:
agent_info = AgentInfoProto()
agent_info.ParseFromString(data[pos:pos + next_pos])
brain_info = BrainInfo.from_agent_proto([agent_info], brain_params)
brain_infos.append(brain_info)
if len(brain_infos) == total_expected:
break
pos += next_pos
obs_decoded += 1
return brain_params, brain_infos, total_expected
def __init__(self):
"""
Load your agent here and initialize anything needed
"""
# Load the configuration and model using ABSOLUTE PATHS
self.configuration_file = '/aaio/data/trainer_config.yaml'
self.model_path = glob.glob('/aaio/data/*/Learner')[0]
print('model_path', self.model_path)
self.brain = BrainParameters(
brain_name='Learner',
camera_resolutions=[{
'height': 84,
'width': 84,
'blackAndWhite': False
}],
num_stacked_vector_observations=1,
vector_action_descriptions=['', ''],
vector_action_space_size=[3, 3],
vector_action_space_type=0, # corresponds to discrete
vector_observation_space_size=3)
self.trainer_params = yaml.load(open(
self.configuration_file))['Learner']
self.trainer_params['keep_checkpoints'] = 0
self.trainer_params['model_path'] = self.model_path
self.policy = PPOPolicy(brain=self.brain,
seed=0,
trainer_params=self.trainer_params,
is_training=False,
load=True)
self.memory_in = None
self.use_recurrent = self.trainer_params['model_architecture'][
'use_recurrent']
self._arena_map = None
if self.trainer_params['model_architecture']['architecture'] in [
'map', 'wba_prize'
]:
self._map_side = self.trainer_params['model_architecture'][
'map_encoding']['map_side']
else:
self._map_side = None
def __init__(self, trainer_config_path, model_path):
self.brain = BrainParameters(
brain_name='Learner',
camera_resolutions=[{
'height': 84,
'width': 84,
'blackAndWhite': False
}],
num_stacked_vector_observations=1,
vector_action_descriptions=['', ''],
vector_action_space_size=[3, 3],
vector_action_space_type=0, # corresponds to discrete
vector_observation_space_size=3)
if ENABLE_VISITED_MAP_IMAGE:
self.brain = add_extra_camera_parameter(
self.brain, USE_FIXED_VISITED_MAP_COORDINATE,
USE_LIDAR_VECTOR_INFO)
self.extra_brain_info = ExtraBrainInfo()
else:
self.extra_brain_info = None
self.trainer_params = yaml.load(open(trainer_config_path))['Learner']
self.trainer_params['keep_checkpoints'] = 0
self.trainer_params['model_path'] = model_path
self.policy = PPOPolicy(brain=self.brain,
seed=0,
trainer_params=self.trainer_params,
is_training=False,
load=True)
self.lidar_estimator = MultiLidarEstimator(
save_dir="saved_lidar", # データパスの指定
n_arenas=1)