241 lines
9.0 KiB
Python
241 lines
9.0 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.distributions.normal import Normal
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class ObservationEncoder(nn.Module):
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def __init__(self, obs_shape, state_size, num_layers=4, num_filters=32, stride=None):
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super().__init__()
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assert len(obs_shape) == 3
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self.state_size = state_size
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layers = []
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for i in range(num_layers):
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input_channels = obs_shape[0] if i == 0 else output_channels
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output_channels = num_filters * (2 ** i)
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layers.append(nn.Conv2d(in_channels=input_channels, out_channels= output_channels, kernel_size=4, stride=2))
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layers.append(nn.ReLU())
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self.convs = nn.Sequential(*layers)
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self.fc = nn.Linear(256 * 3 * 3, 2 * state_size)
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def forward(self, x):
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x = self.convs(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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# Mean and standard deviation
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mean, std = torch.chunk(x, 2, dim=-1)
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std = F.softplus(std)
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std = torch.clamp(std, min=0.0, max=1e5)
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# Normal Distribution
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dist = self.get_dist(mean, std)
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# Sampling via reparameterization Trick
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x = self.reparameterize(mean, std)
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encoded_output = {"sample": x, "distribution": dist}
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return encoded_output
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def reparameterize(self, mu, std):
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eps = torch.randn_like(std)
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return mu + eps * std
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def get_dist(self, mean, std):
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distribution = torch.distributions.Normal(mean, std)
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distribution = torch.distributions.independent.Independent(distribution, 1)
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return distribution
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class ObservationDecoder(nn.Module):
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def __init__(self, state_size, output_shape):
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super().__init__()
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self.state_size = state_size
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self.output_shape = output_shape
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self.input_size = 256 * 3 * 3
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self.in_channels = [self.input_size, 256, 128, 64]
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self.out_channels = [256, 128, 64, 3]
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if output_shape[1] == 84:
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self.kernels = [5, 7, 5, 6]
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self.output_padding = [1, 1, 1, 0]
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elif output_shape[1] == 64:
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self.kernels = [5, 5, 6, 6]
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self.output_padding = [0, 0, 0, 0]
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self.dense = nn.Linear(state_size, self.input_size)
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layers = []
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for i in range(len(self.kernels)):
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layers.append(nn.ConvTranspose2d(in_channels=self.in_channels[i], out_channels=self.out_channels[i],
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kernel_size=self.kernels[i], stride=2, output_padding=self.output_padding[i]))
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if i!=len(self.kernels)-1:
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layers.append(nn.ReLU())
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self.convtranspose = nn.Sequential(*layers)
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def forward(self, features):
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out_batch_shape = features.shape[:-1]
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out = self.dense(features)
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out = torch.reshape(out, [-1, self.input_size, 1, 1])
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out = self.convtranspose(out)
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mean = torch.reshape(out, (*out_batch_shape, *self.output_shape))
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out_dist = torch.distributions.independent.Independent(torch.distributions.Normal(mean, 1), len(self.output_shape))
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return out_dist
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class ActionDecoder(nn.Module):
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def __init__(self, state_size, hidden_size, action_size, num_layers=5):
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super().__init__()
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self.state_size = state_size
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self.hidden_size = hidden_size
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self.action_size = action_size
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self.num_layers = num_layers
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self._min_std=torch.Tensor([1e-4])[0]
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self._init_std=torch.Tensor([5])[0]
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self._mean_scale=torch.Tensor([5])[0]
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layers = []
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for i in range(self.num_layers):
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input_channels = state_size if i == 0 else self.hidden_size
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output_channels = self.hidden_size if i!= self.num_layers-1 else 2*action_size
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layers.append(nn.Linear(input_channels, output_channels))
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layers.append(nn.ReLU())
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self.action_model = nn.Sequential(*layers)
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def get_dist(self, mean, std):
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distribution = torch.distributions.Normal(mean, std)
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distribution = torch.distributions.transformed_distribution.TransformedDistribution(distribution, TanhBijector())
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distribution = torch.distributions.independent.Independent(distribution, 1)
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return distribution
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def forward(self, features):
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out = self.action_model(features)
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mean, std = torch.chunk(out, 2, dim=-1)
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raw_init_std = torch.log(torch.exp(self._init_std) - 1)
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action_mean = self._mean_scale * torch.tanh(mean / self._mean_scale)
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action_std = F.softplus(std + raw_init_std) + self._min_std
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dist = self.get_dist(action_mean, action_std)
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sample = dist.rsample()
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return sample
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class TransitionModel(nn.Module):
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def __init__(self, state_size, hidden_size, action_size, history_size):
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super().__init__()
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self.state_size = state_size
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self.hidden_size = hidden_size
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self.action_size = action_size
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self.history_size = history_size
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self.act_fn = nn.ReLU()
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self.fc_state_action = nn.Linear(state_size + action_size, hidden_size)
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self.history_cell = nn.GRUCell(hidden_size + history_size, history_size)
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self.fc_state_prior = nn.Linear(history_size + state_size + action_size, 2 * state_size)
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self.fc_state_posterior = nn.Linear(history_size + state_size + action_size, 2 * state_size)
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def init_states(self, batch_size, device):
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self.prev_state = torch.zeros(batch_size, self.state_size).to(device)
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self.prev_action = torch.zeros(batch_size, self.action_size).to(device)
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self.prev_history = torch.zeros(batch_size, self.history_size).to(device)
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def get_dist(self, mean, std):
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distribution = torch.distributions.Normal(mean, std)
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distribution = torch.distributions.independent.Independent(distribution, 1)
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return distribution
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def imagine_step(self, prev_state, prev_action, prev_history):
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state_action = self.act_fn(self.fc_state_action(torch.cat([prev_state, prev_action], dim=-1)))
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history = self.history_cell(torch.cat([state_action, prev_history], dim=-1), prev_history)
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state_prior = self.fc_state_prior(torch.cat([history, prev_state, prev_action], dim=-1))
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state_prior_mean, state_prior_std = torch.chunk(state_prior, 2, dim=-1)
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state_prior_std = F.softplus(state_prior_std)
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# Normal Distribution
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state_prior_dist = self.get_dist(state_prior_mean, state_prior_std)
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# Sampling via reparameterization Trick
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sample_state_prior = self.reparemeterize(state_prior_mean, state_prior_std)
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prior = {"mean": state_prior_mean, "std": state_prior_std, "sample": sample_state_prior, "history": history, "distribution": state_prior_dist}
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return prior
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def reparemeterize(self, mean, std):
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eps = torch.randn_like(std)
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return mean + eps * std
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class TanhBijector(torch.distributions.Transform):
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def __init__(self):
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super().__init__()
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self.bijective = True
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self.domain = torch.distributions.constraints.real
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self.codomain = torch.distributions.constraints.interval(-1.0, 1.0)
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@property
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def sign(self): return 1.
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def _call(self, x): return torch.tanh(x)
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def atanh(self, x):
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return 0.5 * torch.log((1 + x) / (1 - x))
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def _inverse(self, y: torch.Tensor):
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y = torch.where(
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(torch.abs(y) <= 1.),
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torch.clamp(y, -0.99999997, 0.99999997),
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y)
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y = self.atanh(y)
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return y
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def log_abs_det_jacobian(self, x, y):
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#return 2. * (np.log(2) - x - F.softplus(-2. * x))
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return 2.0 * (torch.log(torch.tensor([2.0])) - x - F.softplus(-2.0 * x))
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class CLUBSample(nn.Module): # Sampled version of the CLUB estimator
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def __init__(self, x_dim, y_dim, hidden_size):
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super(CLUBSample, self).__init__()
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self.p_mu = nn.Sequential(
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nn.Linear(x_dim, hidden_size//2),
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nn.ReLU(),
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nn.Linear(hidden_size//2, hidden_size//2),
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nn.ReLU(),
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nn.Linear(hidden_size//2, y_dim)
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)
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self.p_logvar = nn.Sequential(
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nn.Linear(x_dim, hidden_size//2),
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nn.ReLU(),
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nn.Linear(hidden_size//2, hidden_size//2),
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nn.ReLU(),
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nn.Linear(hidden_size//2, y_dim),
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nn.Tanh()
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)
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def get_mu_logvar(self, x_samples):
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mu = self.p_mu(x_samples)
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logvar = self.p_logvar(x_samples)
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return mu, logvar
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def loglikeli(self, x_samples, y_samples):
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mu, logvar = self.get_mu_logvar(x_samples)
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return (-(mu - y_samples)**2 /logvar.exp()-logvar).sum(dim=1).mean(dim=0)
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def forward(self, x_samples, y_samples):
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mu, logvar = self.get_mu_logvar(x_samples)
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return - self.loglikeli(x_samples, y_samples) |