def load_models(self, filename, path='models'):
path = os.path.join(path, filename)
if not os.path.isfile(path):
raise Exception('No such path:' + path)
temp = torch.load(path)
self.actor_critic.load_state_dict(temp['model'], map_location=self.device)
utls.copy_parameters(self.target_actor_critic, self.actor_critic)
def gradient_update_sarsa(self, batch_size=100):
s, a, r, s1, a1, ano = self.replay.sample_(batch_size)
sa = [np.array(x[0], dtype=np.float32) for x in s]
sb = [np.array(x[1], dtype=np.float32) for x in s]
sa = [Variable(torch.from_numpy(x.reshape((1,) + x.shape)).to(self.device)) for x in sa]
sb = [Variable(torch.from_numpy(x.reshape((1,) + x.shape)).to(self.device)) for x in sb]
a1 = [np.array(x, dtype=np.float32) for x in a1]
a1 = [Variable(torch.from_numpy(x.reshape((1,) + x.shape)).to(self.device)) for x in a1]
a = Variable(torch.from_numpy(np.array(a, dtype=np.float32)).to(self.device))
r = Variable(torch.from_numpy(np.array(r, dtype=np.float32)).to(self.device))
s1a = [np.array(x[0], dtype=np.float32) for x in s1]
s1b = [np.array(x[1], dtype=np.float32) for x in s1]
s1a = [Variable(torch.from_numpy(x.reshape((1,) + x.shape)).to(self.device)) for x in s1a]
s1b = [Variable(torch.from_numpy(x.reshape((1,) + x.shape)).to(self.device)) for x in s1b]
# ac = Variable(torch.from_numpy(np.array(a1, dtype=np.float32))).to(self.device)
self.q_next1 = torch.squeeze(torch.cat([self.target_actor_critic.critic_forward1(x[0], x[1], x[2],
adjust_dim=False,
convert_torch=False,
node_labels=False).detach() for
x in zip(s1a, s1b, a1)], dim=0))
self.q_next2 = torch.squeeze(torch.cat([self.target_actor_critic.critic_forward2(x[0], x[1], x[2],
adjust_dim=False,
convert_torch=False,
node_labels=False).detach() for
x in zip(s1a, s1b, a1)], dim=0))
self.q_next = torch.min(self.q_next1, self.q_next2).detach()
self.q_expected = r + self.gamma * self.q_next
self.r = r
self.q_predicted1 = torch.squeeze(torch.cat([self.actor_critic.forward(x[0], x[1], x[2].view((1,) + x[2].shape),
adjust_dim=False, convert_torch=False,
node_labels=False)[1] for x in
zip(sa, sb, a)], dim=0))
self.q_predicted2 = torch.squeeze(torch.cat([self.actor_critic.forward(x[0], x[1], x[2].view((1,) + x[2].shape),
adjust_dim=False, convert_torch=False,
node_labels=False)[2] for x in
zip(sa, sb, a)], dim=0))
# self.loss_critic = F.smooth_l1_loss(q_predicted, q_expected)
self.loss_critic = F.mse_loss(self.q_predicted1, self.q_expected) + F.mse_loss(self.q_predicted2,
self.q_expected)
# self.actor_critic_opt.zero_grad()
self.critic_opt.zero_grad()
self.loss_critic.backward()
# self.actor_critic_opt.step()
self.critic_opt.step()
utls.copy_parameters(self.target_actor_critic, self.actor_critic, self.eta)
def __init__(self, input_dim, state_dim, action_dim, replayBuff, lr=1e-3, gamma=0.99, eta=1e-3,
gcn_num_layers=2, num_pooling=1, assign_hidden_dim=40, assign_dim=40, num_aggs=1, use_cuda=True):
self.state_dim = state_dim
self.action_dim = action_dim
self.input_dim = input_dim
self.lr = lr
self.it = 0
self.replay = replayBuff
if use_cuda:
self.device = utls.get_torch_device()
else:
self.device = torch.device("cpu")
self.actor_critic = GraphTD3(input_dim=input_dim, node_embedding_dim=action_dim, graph_embedding_dim=state_dim,
use_cuda=use_cuda,
gcn_num_layers=gcn_num_layers, num_pooling=num_pooling, assign_dim=assign_dim,
num_aggs=num_aggs, max_num_nodes=assign_hidden_dim).to(self.device)
self.target_actor_critic = GraphTD3(input_dim=input_dim, node_embedding_dim=action_dim,
graph_embedding_dim=state_dim, use_cuda=use_cuda,
gcn_num_layers=gcn_num_layers, num_pooling=num_pooling,
assign_dim=assign_dim, num_aggs=num_aggs,
max_num_nodes=assign_hidden_dim).to(self.device)
# self.actor_critic_opt = torch.optim.Adam(self.actor_critic.parameters(),self.lr, weight_decay=0.001)
self.critic_opt = torch.optim.Adam(
list(self.actor_critic.critic.parameters()) + list(self.actor_critic.graph_embedder.parameters()), self.lr,
weight_decay=0.001)
# self.actor_opt = torch.optim.Adam(self.actor_critic.actor.parameters(), self.lr, weight_decay=0.001)
self.gamma = gamma
self.eta = eta
utls.copy_parameters(self.target_actor_critic, self.actor_critic)
