model.py

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):
        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_)

    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))
        action = torch.tanh(x_t)
        log_prob = normal.log_prob(x_t)
        # Enforcing Action Bound
        log_prob -= torch.log(1 - action.pow(2) + epsilon)
        log_prob = log_prob.sum(1, keepdim=True)
        return action, log_prob, torch.tanh(mean)

class DeterministicPolicy(nn.Module):
    def __init__(self, num_inputs, num_actions, hidden_dim):
        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_)

    def forward(self, state):
        x = F.relu(self.linear1(state))
        x = F.relu(self.linear2(x))
        mean = torch.tanh(self.mean(x))
        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