def __init__(
self,
state_shape,
units=(32, 32),
lr=0.001,
enable_sn=False,
name="GAIfO",
**kwargs):
"""
Initialize GAIfO
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``(32, 32)``
lr (float): Learning rate. The default is ``0.001``
enable_sn (bool): Whether enable Spectral Normalization. The defailt is ``False``
name (str): The default is ``"GAIfO"``
"""
IRLPolicy.__init__(self, name=name, n_training=1, **kwargs)
self.disc = Discriminator(
state_shape=state_shape,
units=units, enable_sn=enable_sn)
self.optimizer = tf.keras.optimizers.Adam(
learning_rate=lr, beta_1=0.5)
def __init__(
self,
state_shape,
action_dim,
units=[32, 32],
n_latent_unit=32,
lr=5e-5,
kl_target=0.5,
reg_param=0.,
enable_sn=False,
enable_gp=False,
name="VAIL",
**kwargs):
"""
:param enable_sn (bool): If true, add spectral normalization in Dense layer
:param enable_gp (bool): If true, add gradient penalty to loss function
"""
IRLPolicy.__init__(
self, name=name, n_training=10, **kwargs)
self.disc = Discriminator(
state_shape=state_shape, action_dim=action_dim,
units=units, n_latent_unit=n_latent_unit,
enable_sn=enable_sn)
self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
self._kl_target = kl_target
self._reg_param = tf.Variable(reg_param, dtype=tf.float32)
self._step_reg_param = tf.constant(1e-5, dtype=tf.float32)
self._enable_gp = enable_gp
def __init__(self,
state_shape,
units=[32, 32],
lr=0.001,
enable_sn=False,
name="GAIfO",
**kwargs):
IRLPolicy.__init__(self, name=name, n_training=1, **kwargs)
self.disc = Discriminator(state_shape=state_shape,
units=units,
enable_sn=enable_sn)
self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr, beta_1=0.5)
def __init__(self,
state_shape,
action_dim,
units=(32, 32),
n_latent_unit=32,
lr=5e-5,
kl_target=0.5,
reg_param=0.,
enable_sn=False,
enable_gp=False,
name="VAIL",
**kwargs):
"""
Initialize VAIL
Args:
state_shape (iterable of int):
action_dim (int):
units (iterable of int): The default is ``(32, 32)``
lr (float): Learning rate. The default is ``5e-5``
kl_target (float): The default is ``0.5``
reg_param (float): The default is ``0``
enable_sn (bool): Whether enable Spectral Normalization. The defailt is ``False``
enable_gp (bool): Whether loss function includes gradient panalty
name (str): The default is ``"VAIL"``
"""
IRLPolicy.__init__(self, name=name, n_training=10, **kwargs)
self.disc = Discriminator(state_shape=state_shape,
action_dim=action_dim,
units=units,
n_latent_unit=n_latent_unit,
enable_sn=enable_sn)
self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
self._kl_target = kl_target
self._reg_param = tf.Variable(reg_param, dtype=tf.float32)
self._step_reg_param = tf.constant(1e-5, dtype=tf.float32)
self._enable_gp = enable_gp
def get_argument(parser=None):
parser = IRLPolicy.get_argument(parser)
parser.add_argument('--enable-sn', action='store_true')
return parser