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models.py
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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as td
import numpy as np
import utils
class Encoder(nn.Module):
def __init__(self, input_shape, hidden_dims, latent_dims):
super().__init__()
self.latent_dims = latent_dims
self.encoder = nn.Sequential(
nn.Linear(input_shape, hidden_dims),
nn.ELU(), nn.Linear(hidden_dims, hidden_dims),
nn.ELU(), nn.Linear(hidden_dims, 2*latent_dims))
self.std_min = 0.1
self.std_max = 10.0
self.apply(utils.weight_init)
def forward(self, x):
x = self.encoder(x)
mean, std = torch.chunk(x, 2, -1)
mean = 30 * torch.tanh(mean / 30)
std = self.std_max - F.softplus(self.std_max-std)
std = self.std_min + F.softplus(std-self.std_min)
return td.independent.Independent(td.Normal(mean, std), 1)
class ModelPrior(nn.Module):
def __init__(self, latent_dims, action_dims, hidden_dims, num_layers=2):
super().__init__()
self.latent_dims = latent_dims
self.action_dims = action_dims
self.hidden_dims = hidden_dims
self.num_layers = num_layers
self.std_min = 0.1
self.std_max = 10.0
self.model = self._build_model()
self.apply(utils.weight_init)
def _build_model(self):
model = [nn.Linear(self.action_dims + self.latent_dims, self.hidden_dims)]
model += [nn.ELU()]
for i in range(self.num_layers-1):
model += [nn.Linear(self.hidden_dims, self.hidden_dims)]
model += [nn.ELU()]
model += [nn.Linear(self.hidden_dims, 2*self.latent_dims)]
return nn.Sequential(*model)
def forward(self, z, action):
x = torch.cat([z, action], axis=-1)
x = self.model(x)
mean, std = torch.chunk(x, 2, -1)
mean = 30 * torch.tanh(mean / 30)
std = self.std_max - F.softplus(self.std_max-std)
std = self.std_min + F.softplus(std-self.std_min)
return td.independent.Independent(td.Normal(mean, std), 1)
class RewardPrior(nn.Module):
def __init__(self, latent_dims, hidden_dims, action_dims):
super().__init__()
self.reward = nn.Sequential(
nn.Linear(latent_dims + action_dims, hidden_dims), nn.LayerNorm(hidden_dims),
nn.Tanh(), nn.Linear(hidden_dims, hidden_dims),
nn.ELU(), nn.Linear(hidden_dims, 1))
self.apply(utils.weight_init)
def forward(self, z, a):
z_a = torch.cat([z, a], -1)
reward = self.reward(z_a)
return reward
class Discriminator(nn.Module):
def __init__(self, latent_dims, hidden_dims, action_dims):
super().__init__()
self.classifier = nn.Sequential(
nn.Linear(2 * latent_dims + action_dims, hidden_dims), nn.LayerNorm(hidden_dims),
nn.Tanh(), nn.Linear(hidden_dims, hidden_dims),
nn.ELU(), nn.Linear(hidden_dims, 2))
self.apply(utils.weight_init)
def forward(self, z, a, z_next):
x = torch.cat([z, a, z_next], -1)
logits = self.classifier(x)
return logits
def get_reward(self, z, a, z_next):
x = torch.cat([z, a, z_next], -1)
logits = self.classifier(x)
reward = torch.sub(logits[..., 1], logits[..., 0])
return reward.unsqueeze(-1)
class Critic(nn.Module):
def __init__(self, latent_dims, hidden_dims, action_shape):
super().__init__()
self.Q1 = nn.Sequential(
nn.Linear(latent_dims + action_shape, hidden_dims), nn.LayerNorm(hidden_dims),
nn.Tanh(), nn.Linear(hidden_dims, hidden_dims),
nn.ELU(), nn.Linear(hidden_dims, 1))
self.Q2 = nn.Sequential(
nn.Linear(latent_dims + action_shape, hidden_dims), nn.LayerNorm(hidden_dims),
nn.Tanh(), nn.Linear(hidden_dims, hidden_dims),
nn.ELU(), nn.Linear(hidden_dims, 1))
self.apply(utils.weight_init)
def forward(self, x, a):
x_a = torch.cat([x, a], -1)
q1 = self.Q1(x_a)
q2 = self.Q2(x_a)
return q1, q2
class Actor(nn.Module):
def __init__(self, input_shape, hidden_dims, output_shape, low, high):
super(Actor, self).__init__()
self.low = low
self.high = high
self.fc1 = nn.Linear(input_shape, hidden_dims)
self.fc2 = nn.Linear(hidden_dims, hidden_dims)
self.mean = nn.Linear(hidden_dims, output_shape)
self.apply(utils.weight_init)
def forward(self, x, std):
x = F.elu(self.fc1(x))
x = F.elu(self.fc2(x))
mean = torch.tanh(self.mean(x))
std = torch.ones_like(mean) * std
dist = utils.TruncatedNormal(mean, std, self.low, self.high)
return dist
class StochasticActor(nn.Module):
def __init__(self, input_shape, hidden_dims, output_shape, low, high):
super(StochasticActor, self).__init__()
self.low = low
self.high = high
self.fc1 = nn.Linear(input_shape, hidden_dims)
self.fc2 = nn.Linear(hidden_dims, hidden_dims)
self.fc3 = nn.Linear(hidden_dims, 2*output_shape)
self.std_min = np.exp(-5)
self.std_max = np.exp(2)
self.apply(utils.weight_init)
def forward(self, x):
x = F.elu(self.fc1(x))
x = F.elu(self.fc2(x))
x = self.fc3(x)
mean, std = torch.chunk(x, 2, -1)
mean = torch.tanh(mean)
std = self.std_max - F.softplus(self.std_max-std)
std = self.std_min + F.softplus(std-self.std_min)
dist = utils.TruncatedNormal(mean, std, self.low, self.high)
return dist