def __init__(self, n_actions):
super().__init__()
self.n_actions = n_actions
self.feature = tf.keras.Sequential([
ConvBlock(32,
kernel_size=(9, 9),
activation_='relu',
sampling='stride',
normalization='batch'),
ConvBlock(64,
kernel_size=(5, 5),
activation_='relu',
sampling='same',
normalization='batch'),
ConvBlock(64,
kernel_size=(5, 5),
activation_='relu',
sampling='stride',
normalization='batch'),
ConvBlock(128,
kernel_size=(5, 5),
activation_='relu',
sampling='same',
normalization='batch'),
ConvBlock(128,
kernel_size=(5, 5),
activation_='relu',
sampling='stride',
normalization='batch'),
])
self.dense1 = DenseBlock(512,
activation_='relu',
normalization='batch')
self.dense2 = DenseBlock(n_actions)
def __init__(self, latent_dim,
nb_filter=16,
last_activation='tanh',
normalization='batch',
upsampling='deconv',
spectral_norm=False):
super().__init__(latent_dim,
nb_filter,
last_activation,
normalization,
upsampling,
spectral_norm)
self.convs = []
self.dense = DenseBlock(4*4*nb_filter*(2**3),
activation_='relu',
normalization=normalization,
spectral_norm=spectral_norm)
for i in range(1, 4):
_nb_filter = nb_filter*(2**(3-i))
if upsampling == 'subpixel':
_nb_filter *= 4
self.convs.append(ConvBlock(_nb_filter,
kernel_size=(5, 5),
sampling=upsampling,
**self.conv_block_params))
self.last_conv = ConvBlock(1,
kernel_size=(1, 1),
**self.last_conv_block_params)
def __init__(self,
output_dim,
nb_filter=16,
normalization='batch',
downsampling='stride'):
super().__init__(output_dim, nb_filter, normalization, downsampling)
norm_params = {'center': False, 'scale': False}
self.convs = [
ConvBlock(nb_filter * (2**i),
kernel_size=(5, 5),
kernel_regularizer=tf.keras.regularizers.l2(1.),
norm_params=norm_params,
**self.conv_block_params) for i in range(3)
]
self.dense1 = DenseBlock(
128,
activation_='lrelu',
kernel_regularizer=tf.keras.regularizers.l2(1.),
use_bias=False,
norm_params=norm_params,
normalization=self.normalization)
self.dense2 = DenseBlock(
self.output_dim,
kernel_regularizer=tf.keras.regularizers.l2(1.),
use_bias=False)
def __init__(self, n_actions):
super().__init__()
self.n_actions = n_actions
self.feature = tf.keras.Sequential([
ConvBlock(32,
kernel_size=(9, 9),
activation_='relu',
sampling='stride',
normalization='batch'),
ConvBlock(64,
kernel_size=(5, 5),
activation_='relu',
sampling='same',
normalization='batch'),
ConvBlock(64,
kernel_size=(5, 5),
activation_='relu',
sampling='stride',
normalization='batch'),
ConvBlock(128,
kernel_size=(5, 5),
activation_='relu',
sampling='same',
normalization='batch'),
ConvBlock(128,
kernel_size=(5, 5),
activation_='relu',
sampling='stride',
normalization='batch'),
tf.keras.layers.Flatten()
])
self.value_branch = tf.keras.Sequential([
DenseBlock(512, activation_='relu', normalization='batch'),
DenseBlock(1),
])
self.advantage_brach = tf.keras.Sequential([
DenseBlock(512, activation_='relu', normalization='batch'),
DenseBlock(n_actions),
])
def __init__(self,
latent_dim=2,
nb_filter=32,
last_activation='tanh',
normalization='batch',
downsampling='stride',
upsampling='deconv'):
super().__init__(latent_dim, nb_filter, last_activation, normalization,
downsampling, upsampling)
self.feature_shape = (2, 2, nb_filter * (2**3))
self.downs = [
ConvBlock(nb_filter * (2**i),
kernel_size=(3, 3),
sampling=downsampling,
**self.conv_block_params) for i in range(4)
]
self.dense_down1 = DenseBlock(64,
activation_='lrelu',
normalization=normalization)
self.dense_down2 = DenseBlock(latent_dim)
self.dense_up1 = DenseBlock(64,
activation_='lrelu',
normalization=normalization)
self.dense_up2 = DenseBlock(self.feature_shape[0] *
self.feature_shape[1] *
self.feature_shape[2],
activation_='lrelu',
normalization=normalization)
self.ups = [
ConvBlock(nb_filter * (2**(3 - i)),
kernel_size=(3, 3),
sampling=upsampling,
**self.conv_block_params) for i in range(4)
]
self.last_conv = ConvBlock(1,
kernel_size=(1, 1),
activation_=last_activation)
def __init__(self,
nb_filter=16,
normalization='batch',
downsampling='stride',
spectral_norm=False):
super().__init__(nb_filter, normalization, downsampling, spectral_norm)
self.convs = []
for i in range(4):
_nb_filter = nb_filter * (2**i)
self.convs.append(
ConvBlock(_nb_filter,
kernel_size=(5, 5),
sampling=downsampling,
**self.conv_block_params))
self.dense = DenseBlock(1, spectral_norm=spectral_norm)