def __init__(self, act_dim, max_action):
hidden_dim_1, hidden_dim_2 = 64, 64
self.fc1 = layers.fc(size=hidden_dim_1, act='tanh')
self.fc2 = layers.fc(size=hidden_dim_2, act='tanh')
self.fc3 = layers.fc(size=act_dim, act='tanh')
self.max_action = max_action
def __init__(self, act_dim):
hid1_size = act_dim * 50
hid2_size = act_dim * 50
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
hidden_size = 256
self.net1 = layers.fc(size=hidden_size, act='relu')
self.net2 = layers.fc(size=hidden_size, act='relu')
self.net3 = layers.fc(size=hidden_size, act='relu')
self.net4 = layers.fc(size=hidden_size, act='relu')
self.net5 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
self.act_dim = act_dim
self.conv1 = layers.conv2d(num_filters=32,
filter_size=2,
stride=1,
act='relu')
self.conv2 = layers.conv2d(num_filters=8,
filter_size=4,
stride=1,
padding=2,
act='relu')
self.conv3 = layers.conv2d(num_filters=8,
filter_size=4,
stride=1,
padding=2,
act='relu')
self.conv4 = layers.conv2d(num_filters=8,
filter_size=4,
stride=1,
padding=2,
act='relu')
self.fc1 = layers.fc(size=act_dim, act=None)
self.fc2 = layers.fc(size=act_dim, act=None)
self.fc3 = layers.fc(size=1, act='sigmoid')
def __init__(self, act_dim):
hid1_size = 128
hid2_size = 128
self.fc1 = layers.fc(size=hid1_size, act='tanh')
self.fc2 = layers.fc(size=hid2_size, act='tanh')
self.fc3 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
hid1_size = 256
hid2_size = 256
self.fc1 = layers.fc(size=hid1_size, act="relu")
self.fc2 = layers.fc(size=hid2_size, act="relu")
self.fc3 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
hid1_size = 128
hid2_size = 128
# 神经网络共有三层:fc(128)+fc(128)+fc(act_dim)
self.fc1 = layers.fc(size=hid1_size, act="relu")
self.fc2 = layers.fc(size=hid2_size, act="relu")
self.fc3 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
self.act_dim = act_dim
hid1_size = 32
hid2_size = 32
self.fc1 = layers.fc(size=hid1_size, act='tanh')
self.fc2 = layers.fc(size=hid2_size, act='tanh')
self.fcOut = layers.fc(size=act_dim, act='softmax')
def __init__(self, config):
self.n_actions = config['n_actions']
self.rnn_hidden_dim = config['rnn_hidden_dim']
self.fc1 = layers.fc(size=self.rnn_hidden_dim, act=None, name='fc1')
self.gru = layers.GRUCell(hidden_size=self.rnn_hidden_dim, name='gru')
self.fc2 = layers.fc(size=self.n_actions, act=None, name='fc2')
def __init__(self, act_dim):
hid1_size = 256
hid2_size = 256
self.fc1 = layers.fc(size=hid1_size, act='tanh')
self.fc2 = layers.fc(size=hid2_size, act='tanh')
self.fc3 = layers.fc(size=act_dim)
def __init__(self, act_dim):
hidden_size_1 = 128
hidden_size_2 = 128
self.fc1 = layers.fc(size=hidden_size_1, act='relu')
self.fc2 = layers.fc(size=hidden_size_2, act='relu')
self.fc3 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
hid1_size = 128
hid2_size = 128
# 3层全连接网络
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
act_dim = act_dim
hidden_dim_1 = hidden_dim_2 = 128
self.fc1 = layers.fc(size=hidden_dim_1, act='tanh')
self.fc2 = layers.fc(size=hidden_dim_2, act='tanh')
self.fc3 = layers.fc(size=act_dim, act="softmax")
def __init__(self):
self.fc1 = layers.fc(size=256,
act=None,
param_attr=ParamAttr(name='fc1.w'),
bias_attr=ParamAttr(name='fc1.b'))
self.fc_tuple = (layers.fc(size=128,
act=None,
param_attr=ParamAttr(name='fc2.w'),
bias_attr=ParamAttr(name='fc2.b')),
(layers.fc(size=1,
act=None,
param_attr=ParamAttr(name='fc3.w'),
bias_attr=ParamAttr(name='fc3.b')),
10), 10)
self.fc_dict = {
'k1':
layers.fc(size=128,
act=None,
param_attr=ParamAttr(name='fc4.w'),
bias_attr=ParamAttr(name='fc4.b')),
'k2': {
'k22':
layers.fc(size=1,
act=None,
param_attr=ParamAttr(name='fc5.w'),
bias_attr=ParamAttr(name='fc5.b'))
},
'k3':
1,
}
def __init__(self, act_dim):
act_dim = act_dim
hid1_size = act_dim * 10
self.fc1 = layers.fc(size=hid1_size, act='tanh')
#self.fc2 = layers.fc(size=hid1_size, act='tanh')
self.fc3 = layers.fc(size=act_dim, act='softmax')
def __init__(self, act_dim, algo='DQN'):
self.act_dim = act_dim
self.conv1 = layers.conv2d(num_filters=32,
filter_size=5,
stride=1,
padding=2,
act='relu')
self.conv2 = layers.conv2d(num_filters=32,
filter_size=5,
stride=1,
padding=2,
act='relu')
self.conv3 = layers.conv2d(num_filters=64,
filter_size=4,
stride=1,
padding=1,
act='relu')
self.conv4 = layers.conv2d(num_filters=64,
filter_size=3,
stride=1,
padding=1,
act='relu')
self.algo = algo
if algo == 'Dueling':
self.fc1_adv = layers.fc(size=512, act='relu')
self.fc2_adv = layers.fc(size=act_dim)
self.fc1_val = layers.fc(size=512, act='relu')
self.fc2_val = layers.fc(size=1)
else:
self.fc1 = layers.fc(size=act_dim)
def __init__(self, act_dim):
hid1_size = 400
hid2_size = 300
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act='tanh')
def __init__(self, act_dim):
hid1_size = 256
hid2_size = 128
# 3 fully connected layers
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act=None)
def __init__(self, act_dim):
super(DecoderGeneratorModel, self).__init__()
self.conv1 = self.conv2d_helper(num_filters=32,
filter_size=8,
stride=4,
padding=1,
act='relu')
self.conv2 = self.conv2d_helper(num_filters=64,
filter_size=4,
stride=2,
padding=2,
act='relu')
self.conv3 = self.conv2d_helper(num_filters=64,
filter_size=3,
stride=1,
padding=0,
act='relu')
self.flat = self.flatten_helper(axis=1)
self.fc = self.fc_helepr(size=512, act='relu')
self.policy_fc = layers.fc(size=act_dim)
self.value_fc = layers.fc(size=1)
# self.encoder = [self.conv1, self.conv2, self.conv3, self.flat, self.fc]
self.decoder = self.decoder_generator()
def __init__(self):
hid1_size = 400
hid2_size = 300
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=1, act=None)
def __init__(self, act_dim):
# 配置model结构
hid_size = 100
self.fc1 = layers.fc(size=hid_size, act='relu')
self.fc2 = layers.fc(size=act_dim, act='tanh')
def __init__(self, act_dim):
hid1_size = 32
hid2_size = 64
# 3层全连接网络
self.fc1 = layers.fc(size=hid1_size, act='relu', name="fc1")
self.fc2 = layers.fc(size=hid2_size, act='relu', name="fc2")
self.fc3 = layers.fc(size=act_dim, act=None, name="fc3")
def __init__(self, act_dim):
act_dim = act_dim
hid1_size = 512
hid2_size = 128
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act='softmax')
def __init__(self, act_dim):
self.act_dim = act_dim
hid1_size = 128
hid2_size = 128
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=self.act_dim, act=None)
def __init__(self, act_dim):
self._act_dim = act_dim
self._fc_1 = layers.fc(size=512, act='relu')
self._fc_2 = layers.fc(size=256, act='relu')
self._fc_3 = layers.fc(size=128, act='tanh')
self.value_fc = layers.fc(size=1)
self.policy_fc = layers.fc(size=act_dim)
def __init__(self, act_num):
"""
init
:param act_num:
"""
self.fc1 = layers.fc(size=64, act='relu')
self.fc2 = layers.fc(size=64, act='relu')
self.fc3 = layers.fc(size=act_num, act=None)
def __init__(self, act_dim):
hid1_size = 400
hid2_size = 300
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.mean_linear = layers.fc(size=act_dim)
self.log_std_linear = layers.fc(size=act_dim)
def __init__(self, act_dim):
hid1_size = 32
hid2_size = 32
# 3层全连接网络
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act='tanh')
def __init__(self, act_dim):
hid0_size = 64
hid1_size = 32
hid2_size = 16
self.fc0 = layers.fc(size=hid0_size, act='relu', name="catfc0")
self.fc1 = layers.fc(size=hid1_size, act='relu', name="catfc1")
self.fc2 = layers.fc(size=hid2_size, act='relu', name="catfc2")
self.fc3 = layers.fc(size=act_dim, act=None, name="catfc3")
def __init__(self):
######################################################################
######################################################################
#
# 4. 请配置model结构
hid_size = 128
self.fc1 = layers.fc(size=hid_size, act='relu')
self.fc2 = layers.fc(size=1, act=None)
