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)