def _init_agents(self):
# parameter sharing
self.embedding = Embedding_Layer(self.input_dim,
self.hidden_dim).to(self.device)
self.rnn = RNN_Model(self.hidden_dim, self.num_agents).to(self.device)
self.attention = Attention_Model(self.hidden_dim).to(self.device)
self.embedding_target = Embedding_Layer(
self.input_dim, self.hidden_dim).to(self.device)
self.rnn_target = RNN_Model(self.hidden_dim,
self.num_agents).to(self.device)
self.attention_target = Attention_Model(self.hidden_dim).to(
self.device)
Dueling_DDQN_Learner.copy_network(self.embedding,
self.embedding_target)
Dueling_DDQN_Learner.copy_network(self.rnn, self.rnn_target)
Dueling_DDQN_Learner.copy_network(self.attention,
self.attention_target)
self.share_para = chain(self.embedding.parameters(),
self.attention.parameters(),
self.rnn.parameters())
self.all_para = chain(self.embedding.parameters(),
self.attention.parameters(),
self.rnn.parameters())
# init the optimizer
for i in range(self.num_agents):
self.agents.append(Dueling_DDQN_Learner(self.config))
self.all_para = chain(self.all_para,
self.agents[i].get_q_network().parameters())
# self.all_para = chain(self.all_para)
self.share_optimizer = optim.RMSprop(self.all_para,
lr=self.lr,
weight_decay=1e-4)
def _init_agents(self):
self.embedding = Embedding_Layer(self.input_dim,
self.hidden_dim).to(self.device)
self.attention = Attention_Model(self.hidden_dim).to(self.device)
self.temporal_attention = Attention_Model(self.hidden_dim).to(
self.device)
self.embedding_target = Embedding_Layer(
self.input_dim, self.hidden_dim).to(self.device)
self.attention_target = Attention_Model(self.hidden_dim).to(
self.device)
self.temporal_attention_target = Attention_Model(self.hidden_dim).to(
self.device)
Dueling_DDQN_Learner.copy_network(self.embedding,
self.embedding_target)
Dueling_DDQN_Learner.copy_network(self.attention,
self.attention_target)
Dueling_DDQN_Learner.copy_network(self.temporal_attention,
self.temporal_attention_target)
for i in range(self.num_agents):
q_network = Double_Attention_Model(self.input_dim, self.output_dim,
self.hidden_dim).to(self.device)
q_network_target = Double_Attention_Model(
self.input_dim, self.output_dim,
self.hidden_dim).to(self.device)
q_network.set_layer_para(self.embedding, self.attention,
self.temporal_attention)
q_network_target.set_layer_para(self.embedding_target,
self.attention_target,
self.temporal_attention_target)
self.agents[i].set_q_network(q_network, q_network_target)
# def change_mode(self):
# self.q_network.change_mode()
# self.q_network_target.change_mode()
# for i in range(self.num_agents):
# self.agents[i].q_network_current.change_mode()
# self.agents[i].q_network_target.change_mode()
# self.embedding = Embedding_Layer(self.input_dim, self.hidden_dim[0])
# self.attention = Attention_Layer(self.hidden_dim[0], self.hidden_dim[1], self.hidden_dim[2])
# self.embedding_target = Embedding_Layer(self.input_dim, self.hidden_dim[0])
# self.attention_target = Attention_Layer(self.hidden_dim[0], self.hidden_dim[1], self.hidden_dim[2])
# Dueling_DDQN_Learner.copy_network(self.embedding, self.embedding_target)
# Dueling_DDQN_Learner.copy_network(self.attention, self.attention_target)
# def get_action(self, i, obs):
# return self.agents[i].step(obs)
# def store_experience(self, i, obs, action, reward, next_obs, is_done):
# self.agents[i].store_experience(obs, action, reward, next_obs, is_done)
def __init__(self, input_dim, output_dim, hidden_dim):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.embedding = Embedding_Layer(self.input_dim, self.hidden_dim)
self.rnn = RNN_Model(self.hidden_dim, self.hidden_dim)
self.attention = Attention_Model(self.hidden_dim)
self.relu = nn.ReLU()
self.linear_out = nn.Linear(self.hidden_dim, self.output_dim)
self.hidden, self.cell = None, None
def __init__(self, input_dim, output_dim, hidden_dim):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.hidden_dim = hidden_dim
self.pre_train = False
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
# if embedding_layer:
# self.embedding = embedding_layer
# self.attention = attention_layer
# self.linear1 = nn.Linear(self.input_dim, self.output_dim)
# else:
self.embedding = Embedding_Layer(self.input_dim, self.hidden_dim)
self.attention = Attention_Model(self.hidden_dim)
self.relu = nn.ReLU()
self.linear1 = nn.Linear(self.hidden_dim, self.output_dim)
self.attention_score = None