def prepare_train_data_method(self,
window_data: List = [],
word2index: dict = {},
weighting_dic: dict = {},
X_ik: dict = {}):
u_p = []
v_p = []
co_p = []
weight_p = []
# Reference
# view
# http://pytorch.org/docs/master/tensors.html#torch.Tensor.view
for pair in window_data:
u_p.append(prepare_word(pair[0], word2index).view(1, -1))
v_p.append(prepare_word(pair[1], word2index).view(1, -1))
try:
cooc = X_ik[pair]
except:
cooc = 1
co_p.append(torch.log(Variable(FloatTensor([cooc]))).view(1, -1))
weight_p.append(
Variable(FloatTensor([weighting_dic[pair]])).view(1, -1))
train_data = list(zip(u_p, v_p, co_p, weight_p))
return train_data
def GAE(reward, value, mask, gamma, lam):
adv = FloatTensor(reward.shape)
delta = FloatTensor(reward.shape)
# pre_value, pre_adv = 0, 0
pre_value = torch.zeros(reward.shape[1:], device=device)
pre_adv = torch.zeros(reward.shape[1:], device=device)
for i in reversed(range(reward.shape[0])):
delta[i] = reward[i] + gamma * pre_value * mask[i] - value[i]
adv[i] = delta[i] + gamma * lam * pre_adv * mask[i]
pre_adv = adv[i, ...]
pre_value = value[i, ...]
returns = value + adv
adv = (adv - adv.mean()) / adv.std()
return adv, returns
def get_net_log_prob(self, net_input_state, net_input_discrete_action,
net_input_continuous_action):
net = getattr(self, net_name)
n_action_dim = getattr(self, 'n_' + action_name)
discrete_action_dim = getattr(self, 'discrete_' + action_name + '_dim')
sections = getattr(self, 'discrete_' + action_name + '_sections')
continuous_action_log_std = getattr(
self, net_name + '_' + action_name + '_std')
discrete_action_probs_with_continuous_mean = net(net_input_state)
discrete_actions_log_prob = 0
continuous_actions_log_prob = 0
if discrete_action_dim != 0:
dist = MultiOneHotCategorical(
discrete_action_probs_with_continuous_mean[
..., :discrete_action_dim], sections)
discrete_actions_log_prob = dist.log_prob(
net_input_discrete_action)
if n_action_dim - discrete_action_dim != 0:
continuous_actions_mean = discrete_action_probs_with_continuous_mean[
..., discrete_action_dim:]
continuous_log_std = continuous_action_log_std.expand_as(
continuous_actions_mean)
continuous_actions_std = torch.exp(continuous_log_std)
continuous_dist = MultivariateNormal(
continuous_actions_mean,
torch.diag_embed(continuous_actions_std))
continuous_actions_log_prob = continuous_dist.log_prob(
net_input_continuous_action)
return FloatTensor(discrete_actions_log_prob +
continuous_actions_log_prob).unsqueeze(-1)
def get_net_action(self, state, size=1):
net = getattr(self, net_name)
n_action_dim = getattr(self, 'n_' + action_name)
discrete_action_dim = getattr(self, 'discrete_' + action_name + '_dim')
sections = getattr(self, 'discrete_' + action_name + '_sections')
continuous_action_log_std = getattr(
self, net_name + '_' + action_name + '_std')
discrete_action_probs_with_continuous_mean = net(state)
discrete_actions = torch.empty((size, 0), device=self.device)
continuous_actions = torch.empty((size, 0), device=self.device)
discrete_actions_log_prob = 0
continuous_actions_log_prob = 0
if discrete_action_dim != 0:
dist = MultiOneHotCategorical(
discrete_action_probs_with_continuous_mean[
..., :discrete_action_dim], sections)
discrete_actions = dist.sample()
discrete_actions_log_prob = dist.log_prob(discrete_actions)
if n_action_dim - discrete_action_dim != 0:
continuous_actions_mean = discrete_action_probs_with_continuous_mean[
..., discrete_action_dim:]
continuous_log_std = continuous_action_log_std.expand_as(
continuous_actions_mean)
continuous_actions_std = torch.exp(continuous_log_std)
continuous_dist = MultivariateNormal(
continuous_actions_mean,
torch.diag_embed(continuous_actions_std))
continuous_actions = continuous_dist.sample()
continuous_actions_log_prob = continuous_dist.log_prob(
continuous_actions)
return discrete_actions, continuous_actions, FloatTensor(
discrete_actions_log_prob +
continuous_actions_log_prob).unsqueeze(-1)
def get_policy_net_log_prob(self, net_input_state,
net_input_discrete_action,
net_input_continuous_action):
net = self.policy
n_action_dim = args.n_continuous_action + args.n_discrete_action
discrete_action_dim = args.n_discrete_action
sections = discrete_action_sections
continuous_action_log_std = self.policy_net_action_std
discrete_action_probs_with_continuous_mean = net(net_input_state)
discrete_actions_log_prob = 0
continuous_actions_log_prob = 0
if discrete_action_dim != 0:
dist = MultiOneHotCategorical(
discrete_action_probs_with_continuous_mean[
..., :discrete_action_dim], sections)
discrete_actions_log_prob = dist.log_prob(
net_input_discrete_action)
if n_action_dim - discrete_action_dim != 0:
continuous_actions_mean = discrete_action_probs_with_continuous_mean[
..., discrete_action_dim:]
continuous_log_std = continuous_action_log_std.expand_as(
continuous_actions_mean)
continuous_actions_std = torch.exp(continuous_log_std)
continuous_dist = MultivariateNormal(
continuous_actions_mean,
torch.diag_embed(continuous_actions_std))
continuous_actions_log_prob = continuous_dist.log_prob(
net_input_continuous_action)
return FloatTensor(discrete_actions_log_prob +
continuous_actions_log_prob).unsqueeze(-1)
def get_action(self, state, num_trajs=1):
net = self.policy
n_action_dim = args.n_continuous_action + args.n_discrete_action
discrete_action_dim = args.n_discrete_action
sections = discrete_action_sections
continuous_action_log_std = self.policy_net_action_std
discrete_action_probs_with_continuous_mean = self.policy(state)
discrete_actions = torch.empty((num_trajs, 0), device=device)
continuous_actions = torch.empty((num_trajs, 0), device=device)
discrete_actions_log_prob = 0
continuous_actions_log_prob = 0
if discrete_action_dim != 0:
dist = MultiOneHotCategorical(
discrete_action_probs_with_continuous_mean[
..., :discrete_action_dim], sections)
discrete_actions = dist.sample()
discrete_actions_log_prob = dist.log_prob(discrete_actions)
if n_action_dim - discrete_action_dim != 0:
continuous_actions_mean = discrete_action_probs_with_continuous_mean[
..., discrete_action_dim:]
continuous_log_std = continuous_action_log_std.expand_as(
continuous_actions_mean)
continuous_actions_std = torch.exp(continuous_log_std)
continuous_dist = MultivariateNormal(
continuous_actions_mean,
torch.diag_embed(continuous_actions_std))
continuous_actions = continuous_dist.sample()
continuous_actions_log_prob = continuous_dist.log_prob(
continuous_actions)
return discrete_actions, continuous_actions, FloatTensor(
discrete_actions_log_prob +
continuous_actions_log_prob).unsqueeze(-1)
def get_probs(logging_policy, x, clip_threshold, tao):
# Forward pass on logging policy
logging_output = logging_policy(Variable(FloatTensor([normalize(x)
]))).data[0]
# Do a temperature softmax
logging_output = logging_output * tao
pi_o = F.softmax(logging_output, dim=0)
pi_o, num_z = clip_and_renorm(pi_o, clip_threshold)
return pi_o, num_z
def sample_image(args, generator, n_row, batches_done):
"""Saves a grid of generated digits ranging from 0 to n_classes"""
# Sample noise
z = Variable(
FloatTensor(np.random.normal(0, 1, (n_row**2, args.latent_dim))))
# Get labels for the n rows
labels = np.array([num for _ in range(n_row) for num in range(n_row)])
labels = Variable(LongTensor(labels))
gen_imgs = generator(z, labels)
save_image(gen_imgs.data,
"images/%d.png" % batches_done,
nrow=n_row,
normalize=True)
def get_net_action(self, state, num_trajs=1):
net = getattr(self, net_name)
n_action_dim = getattr(self, 'n_' + action_name)
onehot_action_dim = getattr(self, 'onehot_' + action_name + '_dim')
multihot_action_dim = getattr(self, 'multihot_' + action_name + '_dim')
sections = getattr(self, 'onehot_' + action_name + '_sections')
continuous_action_log_std = getattr(
self, net_name + '_' + action_name + '_std')
onehot_action_probs_with_continuous_mean = net(state)
onehot_actions = torch.empty((num_trajs, 0), device=self.device)
multihot_actions = torch.empty((num_trajs, 0), device=self.device)
continuous_actions = torch.empty((num_trajs, 0), device=self.device)
onehot_actions_log_prob = 0
multihot_actions_log_prob = 0
continuous_actions_log_prob = 0
if onehot_action_dim != 0:
dist = MultiOneHotCategorical(
onehot_action_probs_with_continuous_mean[
..., :onehot_action_dim], sections)
onehot_actions = dist.sample()
onehot_actions_log_prob = dist.log_prob(onehot_actions)
if multihot_action_dim != 0:
multihot_actions_prob = torch.sigmoid(
onehot_action_probs_with_continuous_mean[
...,
onehot_action_dim:onehot_action_dim + multihot_action_dim])
dist = torch.distributions.bernoulli.Bernoulli(
probs=multihot_actions_prob)
multihot_actions = dist.sample()
multihot_actions_log_prob = dist.log_prob(multihot_actions).sum(
dim=1)
if n_action_dim - onehot_action_dim - multihot_action_dim != 0:
continuous_actions_mean = onehot_action_probs_with_continuous_mean[
..., onehot_action_dim + multihot_action_dim:]
continuous_log_std = continuous_action_log_std.expand_as(
continuous_actions_mean)
continuous_actions_std = torch.exp(continuous_log_std)
continuous_dist = MultivariateNormal(
continuous_actions_mean,
torch.diag_embed(continuous_actions_std))
continuous_actions = continuous_dist.sample()
continuous_actions_log_prob = continuous_dist.log_prob(
continuous_actions)
return onehot_actions, multihot_actions, continuous_actions, FloatTensor(
onehot_actions_log_prob + multihot_actions_log_prob +
continuous_actions_log_prob).unsqueeze(-1)
def get_net_log_prob(self, net_input_state, net_input_onehot_action,
net_input_multihot_action,
net_input_continuous_action):
net = getattr(self, net_name)
n_action_dim = getattr(self, 'n_' + action_name)
onehot_action_dim = getattr(self, 'onehot_' + action_name + '_dim')
multihot_action_dim = getattr(self, 'multihot_' + action_name + '_dim')
sections = getattr(self, 'onehot_' + action_name + '_sections')
continuous_action_log_std = getattr(
self, net_name + '_' + action_name + '_std')
onehot_action_probs_with_continuous_mean = net(net_input_state)
onehot_actions_log_prob = 0
multihot_actions_log_prob = 0
continuous_actions_log_prob = 0
if onehot_action_dim != 0:
dist = MultiOneHotCategorical(
onehot_action_probs_with_continuous_mean[
..., :onehot_action_dim], sections)
onehot_actions_log_prob = dist.log_prob(net_input_onehot_action)
if multihot_action_dim != 0:
multihot_actions_prob = torch.sigmoid(
onehot_action_probs_with_continuous_mean[
...,
onehot_action_dim:onehot_action_dim + multihot_action_dim])
dist = torch.distributions.bernoulli.Bernoulli(
probs=multihot_actions_prob)
multihot_actions_log_prob = dist.log_prob(
net_input_multihot_action).sum(dim=1)
if n_action_dim - onehot_action_dim - multihot_action_dim != 0:
continuous_actions_mean = onehot_action_probs_with_continuous_mean[
..., onehot_action_dim + multihot_action_dim:]
continuous_log_std = continuous_action_log_std.expand_as(
continuous_actions_mean)
continuous_actions_std = torch.exp(continuous_log_std)
continuous_dist = MultivariateNormal(
continuous_actions_mean,
torch.diag_embed(continuous_actions_std))
continuous_actions_log_prob = continuous_dist.log_prob(
net_input_continuous_action)
return FloatTensor(onehot_actions_log_prob +
multihot_actions_log_prob +
continuous_actions_log_prob).unsqueeze(-1)
def GAE(reward, value, mask, gamma, lam):
# adv = FloatTensor(reward.shape, device=device)
# delta = FloatTensor(reward.shape, device=device)
# # pre_value, pre_adv = 0, 0
# pre_value = torch.zeros(reward.shape[1:], device=device)
# pre_adv = torch.zeros(reward.shape[1:], device=device)
# for i in reversed(range(reward.shape[0])):
# delta[i] = reward[i] + gamma * pre_value * mask[i] - value[i]
# adv[i] = delta[i] + gamma * lam * pre_adv * mask[i]
# pre_adv = adv[i, ...]
# pre_value = value[i, ...]
# returns = value + adv
# adv = (adv - adv.mean()) / adv.std()
reward = reward.reshape(-1, args.sample_traj_length, 1)
value = value.reshape(-1, args.sample_traj_length, 1)
mask = mask.reshape(-1, args.sample_traj_length, 1)
adv = FloatTensor(reward.shape, device=device)
delta = FloatTensor(reward.shape, device=device)
# pre_value, pre_adv = 0, 0
pre_value = torch.zeros((reward.shape[0], 1), device=device)
pre_adv = torch.zeros((reward.shape[0], 1), device=device)
for i in reversed(range(reward.shape[1])):
delta[:,
i] = reward[:, i] + gamma * pre_value * mask[:, i] - value[:, i]
adv[:, i] = delta[:, i] + gamma * lam * pre_adv * mask[:, i]
pre_adv = adv[:, i, ...]
pre_value = value[:, i, ...]
returns = value + adv
adv = (adv - adv.mean()) / adv.std()
returns = returns.reshape(-1, 1)
adv = adv.reshape(-1, 1)
return adv, returns
def train(generator, discriminator, dataloader, args, cuda, adversarial_loss,
auxiliary_loss):
optimizer_G = torch.optim.Adam(generator.parameters(),
lr=args.lr,
betas=(args.b1, args.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(),
lr=args.lr,
betas=(args.b1, args.b2))
for epoch in range(args.n_epochs):
for i, (imgs, labels) in enumerate(dataloader):
batch_size = imgs.shape[0]
# Adversarial ground truths
valid = Variable(FloatTensor(batch_size, 1).fill_(1.0),
requires_grad=False)
fake = Variable(FloatTensor(batch_size, 1).fill_(0.0),
requires_grad=False)
# Configure input
real_imgs = Variable(imgs.type(FloatTensor))
labels = Variable(labels.type(LongTensor))
# -----------------
# Train Generator
# -----------------
optimizer_G.zero_grad()
# Sample noise as generator input
z = Variable(
FloatTensor(
np.random.normal(0, 1, (batch_size, args.latent_dim))))
gen_labels = Variable(
LongTensor(np.random.randint(0, args.n_classes, batch_size)))
# Generate a batch of images
gen_imgs = generator(z, gen_labels)
# Loss measures generator's ability to fool the discriminator
validity, pred_label = discriminator(gen_imgs)
g_loss = 0.5 * adversarial_loss(validity, valid) + auxiliary_loss(
pred_label, gen_labels)
g_loss.backward()
optimizer_G.step()
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Loss for real images
real_pred, real_aux = discriminator(real_imgs)
d_real_loss = (adversarial_loss(real_pred, valid) +
auxiliary_loss(real_aux, labels)) / 2
# Loss for fake images
fake_pred, fake_aux = discriminator(gen_imgs.detach())
d_fake_loss = (adversarial_loss(fake_pred, fake) +
auxiliary_loss(fake_aux, gen_labels)) / 2
# Measure discriminator's ability to classify real from generated samples
d_loss = (d_real_loss + d_fake_loss) / 2
# Calculate discriminator accuracy
pred = np.concatenate(
[real_aux.data.cpu().numpy(),
fake_aux.data.cpu().numpy()],
axis=0)
gt = np.concatenate(
[labels.data.cpu().numpy(),
gen_labels.data.cpu().numpy()],
axis=0)
d_acc = np.mean(np.argmax(pred, axis=1) == gt)
d_loss.backward()
optimizer_D.step()
print("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" %
(epoch, args.n_epochs, i, len(dataloader), d_loss.item(),
g_loss.item()))
batches_done = epoch * len(dataloader) + i
if batches_done % args.sample_interval == 0:
sample_image(args,
generator,
n_row=10,
batches_done=batches_done)
def train(game,
num_steps=60000000,
lr=0.00025,
gamma=0.99,
C=20000,
batch_size=32):
env = wrappers.wrap(gym.make(GAMES[game]))
num_actions = env.action_space.n
Q1 = QNetwork(num_actions)
Q2 = QNetwork(num_actions)
Q2.load_state_dict(Q1.state_dict())
if torch.cuda.is_available():
Q1.cuda()
Q2.cuda()
epsilon = Epsilon(1, 0.05, 1000000)
optimizer = torch.optim.Adam(Q1.parameters(), lr=lr)
optimizer.zero_grad()
state1 = env.reset()
t, last_t, loss, episode, score = 0, 0, 0, 0, 0
last_ts, scores = datetime.now(), collections.deque(maxlen=100)
while t < num_steps:
qvalues = Q1(state1)
if random() < epsilon(t):
action = env.action_space.sample()
else:
action = qvalues.data.max(dim=1)[1][0]
q = qvalues[0][action]
state2, reward, done, _info = env.step(action)
score += reward
if not done:
y = gamma * Q2(state2).detach().max(dim=1)[0][0] + reward
state1 = state2
else:
reward = FloatTensor([reward])
y = torch.autograd.Variable(reward, requires_grad=False)
state1 = env.reset()
scores.append(score)
score = 0
episode += 1
loss += torch.nn.functional.smooth_l1_loss(q, y)
t += 1
if done or t % batch_size == 0:
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss = 0
if t % C == 0:
Q2.load_state_dict(Q1.state_dict())
torch.save(Q1.state_dict(), 'qlearning_{}.pt'.format(game))
if t % 1000 == 0:
ts = datetime.now()
datestr = ts.strftime('%Y-%m-%dT%H:%M:%S.%f')
avg = mean(scores) if scores else float('nan')
steps_per_sec = (t - last_t) / (ts - last_ts).total_seconds()
l = '{} step {} episode {} avg last 100 scores: {:.2f} ε: {:.2f}, steps/s: {:.0f}'
print(l.format(datestr, t, episode, avg, epsilon(t),
steps_per_sec))
last_t, last_ts = t, ts
def observation(self, observation):
x = FloatTensor(np.swapaxes(observation, 2, 0))
return torch.autograd.Variable(x, requires_grad=False).unsqueeze(0)