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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
LOG_SIG_MAX = 2
LOG_SIG_MIN = -20
epsilon = 1e-6
# Initialize Policy weights
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class ValueNetwork(nn.Module):
def __init__(self, num_inputs, hidden_dim):
super(ValueNetwork, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, hidden_dim)
self.linear3 = nn.Linear(hidden_dim, 1)
self.apply(weights_init_)
def forward(self, state):
x = F.relu(self.linear1(state))
x = F.relu(self.linear2(x))
x = self.linear3(x)
return x
class QNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim):
super(QNetwork, self).__init__()
# Q1 architecture
self.linear1 = nn.Linear(num_inputs + num_actions, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, hidden_dim)
self.linear3 = nn.Linear(hidden_dim, 1)
# Q2 architecture
self.linear4 = nn.Linear(num_inputs + num_actions, hidden_dim)
self.linear5 = nn.Linear(hidden_dim, hidden_dim)
self.linear6 = nn.Linear(hidden_dim, 1)
self.apply(weights_init_)
def forward(self, state, action):
xu = torch.cat([state, action], 1)
x1 = F.relu(self.linear1(xu))
x1 = F.relu(self.linear2(x1))
x1 = self.linear3(x1)
x2 = F.relu(self.linear4(xu))
x2 = F.relu(self.linear5(x2))
x2 = self.linear6(x2)
return x1, x2
class GaussianPolicy(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim, action_space=None):
super(GaussianPolicy, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, hidden_dim)
self.mean_linear = nn.Linear(hidden_dim, num_actions)
self.log_std_linear = nn.Linear(hidden_dim, num_actions)
self.apply(weights_init_)
# action rescaling
if action_space is None:
self.action_scale = torch.tensor(1.)
self.action_bias = torch.tensor(0.)
else:
self.action_scale = torch.FloatTensor(
(action_space.high - action_space.low) / 2.)
self.action_bias = torch.FloatTensor(
(action_space.high + action_space.low) / 2.)
def forward(self, state):
x = F.relu(self.linear1(state))
x = F.relu(self.linear2(x))
mean = self.mean_linear(x)
log_std = self.log_std_linear(x)
log_std = torch.clamp(log_std, min=LOG_SIG_MIN, max=LOG_SIG_MAX)
return mean, log_std
def sample(self, state):
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(mean, std)
x_t = normal.rsample() # for reparameterization trick (mean + std * N(0,1))
y_t = torch.tanh(x_t)
action = y_t * self.action_scale + self.action_bias
log_prob = normal.log_prob(x_t)
# Enforcing Action Bound
log_prob -= torch.log(self.action_scale * (1 - y_t.pow(2)) + epsilon)
log_prob = log_prob.sum(1, keepdim=True)
mean = torch.tanh(mean) * self.action_scale + self.action_bias
return action, log_prob, mean
def to(self, device):
self.action_scale = self.action_scale.to(device)
self.action_bias = self.action_bias.to(device)
return super(GaussianPolicy, self).to(device)
class DeterministicPolicy(nn.Module):
def __init__(self, num_inputs, num_actions, hidden_dim, action_space=None):
super(DeterministicPolicy, self).__init__()
self.linear1 = nn.Linear(num_inputs, hidden_dim)
self.linear2 = nn.Linear(hidden_dim, hidden_dim)
self.mean = nn.Linear(hidden_dim, num_actions)
self.noise = torch.Tensor(num_actions)
self.apply(weights_init_)
# action rescaling
if action_space is None:
self.action_scale = 1.
self.action_bias = 0.
else:
self.action_scale = torch.FloatTensor(
(action_space.high - action_space.low) / 2.)
self.action_bias = torch.FloatTensor(
(action_space.high + action_space.low) / 2.)
def forward(self, state):
x = F.relu(self.linear1(state))
x = F.relu(self.linear2(x))
mean = torch.tanh(self.mean(x)) * self.action_scale + self.action_bias
return mean
def sample(self, state):
mean = self.forward(state)
noise = self.noise.normal_(0., std=0.1)
noise = noise.clamp(-0.25, 0.25)
action = mean + noise
return action, torch.tensor(0.), mean
def to(self, device):
self.action_scale = self.action_scale.to(device)
self.action_bias = self.action_bias.to(device)
return super(GaussianPolicy, self).to(device)