def __init__(
self, states, actions, max_episode_timesteps=None, parallel_interactions=1, config=None,
recorder=None
):
util.overwrite_staticmethod(obj=self, function='create')
util.overwrite_staticmethod(obj=self, function='load')
# Check whether spec attribute exists
if not hasattr(self, 'spec'):
raise TensorforceError.required_attribute(name='Agent', attribute='spec')
# Tensorforce config
if config is None:
config = dict()
self.config = TensorforceConfig(**config)
# TensorFlow logging
tf.get_logger().setLevel(self.config.tf_log_level)
# TensorFlow eager mode
if self.config.eager_mode:
tf.config.run_functions_eagerly(run_eagerly=True)
# Random seed
if self.config.seed is not None:
random.seed(a=self.config.seed)
np.random.seed(seed=self.config.seed)
tf.random.set_seed(seed=self.config.seed)
super().__init__(
fn_act=None, states=states, actions=actions,
max_episode_timesteps=max_episode_timesteps,
parallel_interactions=parallel_interactions, recorder=recorder
)
def __init__(self):
# first two arguments, if applicable: level, visualize=False
util.overwrite_staticmethod(obj=self, function='create')
self._expect_receive = None
self._actions = None
self._reset_output_check = True
self._execute_output_check = True
def __init__(self,
states,
actions,
max_episode_timesteps=None,
parallel_interactions=1,
config=None,
recorder=None):
util.overwrite_staticmethod(obj=self, function='create')
util.overwrite_staticmethod(obj=self, function='load')
# Check whether spec attribute exists
if not hasattr(self, 'spec'):
raise TensorforceError.required_attribute(name='Agent',
attribute='spec')
# Tensorforce config
if config is None:
config = dict()
self.config = TensorforceConfig(**config)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(self.config.tf_log_level)
# Import tensorflow after setting log level
import tensorflow as tf
# Eager mode
if self.config.eager_mode:
tf.config.experimental_run_functions_eagerly(run_eagerly=True)
# Random seed
if self.config.seed is not None:
random.seed(a=self.config.seed)
np.random.seed(seed=self.config.seed)
tf.random.set_seed(seed=self.config.seed)
super().__init__(fn_act=None,
states=states,
actions=actions,
max_episode_timesteps=max_episode_timesteps,
parallel_interactions=parallel_interactions,
recorder=recorder)
def __init__(
self, states, actions, max_episode_timesteps=None, parallel_interactions=1, config=None,
recorder=None
):
util.overwrite_staticmethod(obj=self, function='create')
util.overwrite_staticmethod(obj=self, function='load')
self.is_initialized = False
# Check whether spec attribute exists
if not hasattr(self, 'spec'):
raise TensorforceError.required_attribute(name='Agent', attribute='spec')
# States/actions, plus single state/action flag
if 'shape' in states:
self.states_spec = dict(state=states)
self.single_state = True
else:
self.states_spec = states
self.single_state = False
if 'type' in actions:
self.actions_spec = dict(action=actions)
self.single_action = True
else:
self.actions_spec = actions
self.single_action = False
# Max episode timesteps
self.max_episode_timesteps = max_episode_timesteps
# Parallel interactions
if isinstance(parallel_interactions, int):
if parallel_interactions <= 0:
raise TensorforceError.value(
name='Agent', argument='parallel_interactions', value=parallel_interactions,
hint='<= 0'
)
self.parallel_interactions = parallel_interactions
else:
raise TensorforceError.type(
name='Agent', argument='parallel_interactions', dtype=type(parallel_interactions)
)
# Config
if config is None:
config = dict()
self.config = TensorforceConfig(**config)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(self.config.tf_log_level)
# Import tensorflow after setting log level
import tensorflow as tf
# Eager mode
if self.config.eager_mode:
tf.config.experimental_run_functions_eagerly(run_eagerly=True)
# Random seed
if self.config.seed is not None:
random.seed(a=self.config.seed)
np.random.seed(seed=self.config.seed)
tf.random.set_seed(seed=self.config.seed)
# Recorder
if recorder is None:
pass
elif not all(key in ('directory', 'frequency', 'max-traces', 'start') for key in recorder):
raise TensorforceError.value(
name='Agent', argument='recorder values', value=list(recorder),
hint='not from {directory,frequency,max-traces,start}'
)
self.recorder_spec = recorder if recorder is None else dict(recorder)
def __init__(self):
# first two arguments, if applicable: level, visualize=False
util.overwrite_staticmethod(obj=self, function='create')
self._max_episode_timesteps = None
self._expect_receive = None
self._actions = None
