187 lines
6.8 KiB
Python
187 lines
6.8 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.distributions.normal import Normal
|
|
|
|
|
|
class ObservationEncoder(nn.Module):
|
|
def __init__(self, obs_shape, state_size, num_layers=4, num_filters=32, stride=None):
|
|
super().__init__()
|
|
|
|
assert len(obs_shape) == 3
|
|
|
|
self.state_size = state_size
|
|
|
|
layers = []
|
|
for i in range(num_layers):
|
|
input_channels = obs_shape[0] if i == 0 else output_channels
|
|
output_channels = num_filters * (2 ** i)
|
|
layers.append(nn.Conv2d(in_channels=input_channels, out_channels= output_channels, kernel_size=4, stride=2))
|
|
layers.append(nn.ReLU())
|
|
|
|
self.convs = nn.Sequential(*layers)
|
|
|
|
self.fc = nn.Linear(256 * 3 * 3, 2 * state_size)
|
|
|
|
def forward(self, x):
|
|
x = self.convs(x)
|
|
x = x.view(x.size(0), -1)
|
|
x = self.fc(x)
|
|
|
|
# Mean and standard deviation
|
|
mean, std = torch.chunk(x, 2, dim=-1)
|
|
std = F.softplus(std)
|
|
std = torch.clamp(std, min=0.0, max=1e5)
|
|
|
|
# Normal Distribution
|
|
x = self.reparameterize(mean, std)
|
|
return x
|
|
|
|
def reparameterize(self, mu, std):
|
|
eps = torch.randn_like(std)
|
|
return mu + eps * std
|
|
|
|
|
|
class ObservationDecoder(nn.Module):
|
|
def __init__(self, state_size, output_shape):
|
|
super().__init__()
|
|
|
|
self.state_size = state_size
|
|
self.output_shape = output_shape
|
|
self.input_size = 256 * 3 * 3
|
|
self.in_channels = [self.input_size, 256, 128, 64]
|
|
self.out_channels = [256, 128, 64, 3]
|
|
|
|
if output_shape[1] == 84:
|
|
self.kernels = [5, 7, 5, 6]
|
|
self.output_padding = [1, 1, 1, 0]
|
|
elif output_shape[1] == 64:
|
|
self.kernels = [5, 5, 6, 6]
|
|
self.output_padding = [0, 0, 0, 0]
|
|
|
|
self.dense = nn.Linear(state_size, self.input_size)
|
|
|
|
layers = []
|
|
for i in range(len(self.kernels)):
|
|
layers.append(nn.ConvTranspose2d(in_channels=self.in_channels[i], out_channels=self.out_channels[i],
|
|
kernel_size=self.kernels[i], stride=2, output_padding=self.output_padding[i]))
|
|
if i!=len(self.kernels)-1:
|
|
layers.append(nn.ReLU())
|
|
|
|
self.convtranspose = nn.Sequential(*layers)
|
|
|
|
|
|
def forward(self, features):
|
|
out_batch_shape = features.shape[:-1]
|
|
out = self.dense(features)
|
|
out = torch.reshape(out, [-1, self.input_size, 1, 1])
|
|
out = self.convtranspose(out)
|
|
|
|
mean = torch.reshape(out, (*out_batch_shape, *self.output_shape))
|
|
out_dist = torch.distributions.independent.Independent(torch.distributions.Normal(mean, 1), len(self.output_shape))
|
|
return out_dist
|
|
|
|
|
|
class TransitionModel(nn.Module):
|
|
def __init__(self, state_size, hidden_size, action_size, history_size):
|
|
super().__init__()
|
|
|
|
self.state_size = state_size
|
|
self.hidden_size = hidden_size
|
|
self.action_size = action_size
|
|
self.history_size = history_size
|
|
self.act_fn = nn.ReLU()
|
|
|
|
self.fc_state_action = nn.Linear(state_size + action_size, hidden_size)
|
|
self.history_cell = nn.GRUCell(hidden_size + history_size, history_size)
|
|
self.fc_state_prior = nn.Linear(history_size + state_size + action_size, 2 * state_size)
|
|
self.fc_state_posterior = nn.Linear(history_size + state_size + action_size, 2 * state_size)
|
|
|
|
def init_states(self, batch_size, device):
|
|
self.prev_state = torch.zeros(batch_size, self.state_size).to(device)
|
|
self.prev_action = torch.zeros(batch_size, self.action_size).to(device)
|
|
self.prev_history = torch.zeros(batch_size, self.history_size).to(device)
|
|
|
|
|
|
def get_dist(self, mean, std):
|
|
distribution = torch.distributions.Normal(mean, std)
|
|
distribution = torch.distributions.independent.Independent(distribution, 1)
|
|
return distribution
|
|
|
|
def imagine_step(self, prev_state, prev_action, prev_history):
|
|
state_action = self.act_fn(self.fc_state_action(torch.cat([prev_state, prev_action], dim=-1)))
|
|
history = self.history_cell(torch.cat([state_action, prev_history], dim=-1), prev_history)
|
|
|
|
state_prior = self.fc_state_prior(torch.cat([history, prev_state, prev_action], dim=-1))
|
|
state_prior_mean, state_prior_std = torch.chunk(state_prior, 2, dim=-1)
|
|
state_prior_std = F.softplus(state_prior_std)
|
|
|
|
sample_state_prior = self.reparemeterize(state_prior_mean, state_prior_std)
|
|
prior = {"mean": state_prior_mean, "std": state_prior_std, "sample": sample_state_prior, "history": history}
|
|
return prior
|
|
|
|
def reparemeterize(self, mean, std):
|
|
eps = torch.randn_like(std)
|
|
return mean + eps * std
|
|
|
|
|
|
class CLUBSample(nn.Module): # Sampled version of the CLUB estimator
|
|
def __init__(self, x_dim, y_dim, hidden_size):
|
|
super(CLUBSample, self).__init__()
|
|
self.p_mu = nn.Sequential(
|
|
nn.Linear(x_dim, hidden_size//2),
|
|
nn.ReLU(),
|
|
nn.Linear(hidden_size//2, y_dim)
|
|
)
|
|
|
|
self.p_logvar = nn.Sequential(
|
|
nn.Linear(x_dim, hidden_size//2),
|
|
nn.ReLU(),
|
|
nn.Linear(hidden_size//2, y_dim),
|
|
nn.Tanh()
|
|
)
|
|
|
|
def get_mu_logvar(self, x_samples):
|
|
mu = self.p_mu(x_samples)
|
|
logvar = self.p_logvar(x_samples)
|
|
return mu, logvar
|
|
|
|
def loglikeli(self, x_samples, y_samples):
|
|
mu, logvar = self.get_mu_logvar(x_samples)
|
|
return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0)
|
|
|
|
def forward(self, x_samples, y_samples):
|
|
mu, logvar = self.get_mu_logvar(x_samples)
|
|
|
|
sample_size = x_samples.shape[0]
|
|
#random_index = torch.randint(sample_size, (sample_size,)).long()
|
|
random_index = torch.randperm(sample_size).long()
|
|
|
|
positive = - (mu - y_samples)**2 / logvar.exp()
|
|
negative = - (mu - y_samples[random_index])**2 / logvar.exp()
|
|
upper_bound = (positive.sum(dim = -1) - negative.sum(dim = -1)).mean()
|
|
return upper_bound/2.
|
|
|
|
def learning_loss(self, x_samples, y_samples):
|
|
return - self.loglikeli(x_samples, y_samples)
|
|
|
|
if __name__ == "__main__":
|
|
encoder = ObservationEncoder((12,84,84), 256)
|
|
x = torch.randn(5000, 12, 84, 84)
|
|
print(encoder(x).shape)
|
|
exit()
|
|
|
|
club = CLUBSample(256, 256 , 512)
|
|
x = torch.randn(100, 256)
|
|
y = torch.randn(100, 256)
|
|
print(club.learning_loss(x, y))
|
|
|
|
x = torch.randn(100, 12, 84, 84)
|
|
y = torch.randn(100, 12, 84, 84)
|
|
x_enc = encoder(x)
|
|
y_enc = encoder(y)
|
|
print(x_enc.shape)
|
|
print(y_enc.shape)
|
|
print(club.learning_loss(x_enc, y_enc))
|
|
|