def __init__(self, num_states, num_actions, args):
self.args = args
self.num_states = num_states
self.num_actions = num_actions
#Set 2 DNNs
self.main_network = network.NAF_Network(self.num_states,
self.num_actions,
self.args.hidden_layer_size)
self.target_network = network.NAF_Network(self.num_states,
self.num_actions,
self.args.hidden_layer_size)
# initialize parameter vector
filename = 'weight_4.pth'
param = torch.load(filename, map_location='cpu')
self.main_network.load_state_dict(param)
#Output information about Actor network and Critic network
print(self.main_network)
self.target_network.load_state_dict(self.main_network.state_dict())
#Set Optimizer
self.optimizer = optim.Adam(self.main_network.parameters(),
lr=self.args.lr)
self.loss_func = nn.MSELoss()
def __init__(self, num_states, num_actions, args):
self.args = args
self.num_states = num_states
self.num_actions = num_actions
#Set 4 DNNs
self.network_1 = network.NAF_Network(self.num_states,self.num_actions,self.args.hidden_layer_size)
self.network_2 = network.NAF_Network(self.num_states,self.num_actions,self.args.hidden_layer_size)
self.network_3 = network.NAF_Network(self.num_states,self.num_actions,self.args.hidden_layer_size)
self.network_4 = network.NAF_Network(self.num_states,self.num_actions,self.args.hidden_layer_size)
#case1
filename_1 ='weight_1.pth'
filename_2 ='weight_2.pth'
filename_3 ='weight_3.pth'
filename_4 ='weight_4.pth'
#case2
#filename_1 ='weight_5.pth'
#filename_2 ='weight_6.pth'
#filename_3 ='weight_7.pth'
#filename_4 ='weight_8.pth'
#case3
#filename_1 ='weight_1.pth'
#filename_2 ='weight_6.pth'
#filename_3 ='weight_7.pth'
#filename_4 ='weight_8.pth'
#case4
#filename_1 ='weight_5.pth'
#filename_2 ='weight_2.pth'
#filename_3 ='weight_7.pth'
#filename_4 ='weight_8.pth'
#case5
#filename_1 ='weight_1.pth'
#filename_2 ='weight_2.pth'
#filename_3 ='weight_7.pth'
#filename_4 ='weight_8.pth'
param_1 = torch.load(filename_1, map_location='cpu')
param_2 = torch.load(filename_2, map_location='cpu')
param_3 = torch.load(filename_3, map_location='cpu')
param_4 = torch.load(filename_4, map_location='cpu')
self.network_1.load_state_dict(param_1)
self.network_2.load_state_dict(param_2)
self.network_3.load_state_dict(param_3)
self.network_4.load_state_dict(param_4)
def __init__(self, num_states, num_actions, args):
self.args = args
self.num_states = num_states
self.num_actions = num_actions
self.network_1 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_2 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
filename_1 = 'weight_1.pth'
filename_2 = 'weight_2.pth'
param_1 = torch.load(filename_1, map_location='cpu')
param_2 = torch.load(filename_2, map_location='cpu')
self.network_1.load_state_dict(param_1)
self.network_2.load_state_dict(param_2)
def __init__(self, num_states, num_actions, args):
self.args = args
self.num_states = num_states
self.num_actions = num_actions
self.main_network = network.NAF_Network(self.num_states,
self.num_actions,
self.args.hidden_layer_size)
filename = 'weight_1.pth'
param = torch.load(filename, map_location='cpu')
self.main_network.load_state_dict(param)
def __init__(self, num_states, num_actions, args):
self.args = args
self.num_states = num_states
self.num_actions = num_actions
self.network_1 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_2 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_3 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_4 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_5 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_6 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_7 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
self.network_8 = network.NAF_Network(self.num_states, self.num_actions,
self.args.hidden_layer_size)
filename_1 = 'weight_1.pth'
filename_2 = 'weight_2.pth'
filename_3 = 'weight_3.pth'
filename_4 = 'weight_4.pth'
filename_5 = 'weight_5.pth'
filename_6 = 'weight_6.pth'
filename_7 = 'weight_7.pth'
filename_8 = 'weight_8.pth'
param_1 = torch.load(filename_1, map_location='cpu')
param_2 = torch.load(filename_2, map_location='cpu')
param_3 = torch.load(filename_3, map_location='cpu')
param_4 = torch.load(filename_4, map_location='cpu')
param_5 = torch.load(filename_5, map_location='cpu')
param_6 = torch.load(filename_6, map_location='cpu')
param_7 = torch.load(filename_7, map_location='cpu')
param_8 = torch.load(filename_8, map_location='cpu')
self.network_1.load_state_dict(param_1)
self.network_2.load_state_dict(param_2)
self.network_3.load_state_dict(param_3)
self.network_4.load_state_dict(param_4)
self.network_5.load_state_dict(param_5)
self.network_6.load_state_dict(param_6)
self.network_7.load_state_dict(param_7)
self.network_8.load_state_dict(param_8)
def __init__(self, num_states, num_actions, args):
self.args = args
self.num_states = num_states
self.num_actions = num_actions
#Set 2 DNNs
self.main_network = network.NAF_Network(self.num_states,
self.num_actions,
self.args.hidden_layer_size)
self.target_network = network.NAF_Network(self.num_states,
self.num_actions,
self.args.hidden_layer_size)
#Output information about Actor network and Critic network
print(self.main_network)
self.target_network.load_state_dict(self.main_network.state_dict())
#Set Optimizer
self.optimizer = optim.Adam(self.main_network.parameters(),
lr=self.args.lr)
self.loss_func = nn.MSELoss()