text: import numpy as np
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  def hidden_init(layer):
      fan_in = layer.weight.data.size()[0]
      lim = 1. / np.sqrt(fan_in)
      return (-lim, lim)
  
  class Actor(nn.Module):
      """Actor (Policy) Model."""
  
      def __init__(self, state_size, action_size, seed, fc1_units=400, fc2_units=300):
          """Initialize parameters and build model.
          Params
          ======
              state_size (int): Dimension of each state
              action_size (int): Dimension of each action
              seed (int): Random seed
              fc1_units (int): Number of nodes in first hidden layer
              fc2_units (int): Number of nodes in second hidden layer
          """
          super(Actor, self).__init__()
          self.seed = torch.manual_seed(seed)
          self.fc1 = nn.Linear(state_size, fc1_units)
          self.fc2 = nn.Linear(fc1_units, fc2_units)
          self.fc3 = nn.Linear(fc2_units, action_size)
          self.reset_parameters()
  
      def reset_parameters(self):
          self.fc1.weight.data.uniform_(*hidden_init(self.fc1))
          self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
          self.fc3.weight.data.uniform_(-3e-3, 3e-3)
  
      def forward(self, state):
          """Build an actor (policy) network that maps states -> actions."""
          x = F.relu(self.fc1(state))
          x = F.relu(self.fc2(x))
          return F.tanh(self.fc3(x))
  
  
  class Critic(nn.Module):
      """Critic (Value) Model."""
  
      def __init__(self, state_size, action_size, seed, fcs1_units=400, fc2_units=300):
          """Initialize parameters and build model.
          Params
          ======
              state_size (int): Dimension of each state
              action_size (int): Dimension of each action
              seed (int): Random seed
              fcs1_units (int): Number of nodes in the first hidden layer
              fc2_units (int): Number of nodes in the second hidden layer
          """
          super(Critic, self).__init__()
          self.seed = torch.manual_seed(seed)
          self.fcs1 = nn.Linear(state_size, fcs1_units)
          self.fc2 = nn.Linear(fcs1_units+action_size, fc2_units)
          self.fc3 = nn.Linear(fc2_units, 1)
          self.reset_parameters()
  
      def reset_parameters(self):
          self.fcs1.weight.data.uniform_(*hidden_init(self.fcs1))
          self.fc2.weight.data.uniform_(*hidden_init(self.fc2))
          self.fc3.weight.data.uniform_(-3e-3, 3e-3)
  
      def forward(self, state, action):
          """Build a critic (value) network that maps (state, action) pairs -> Q-values."""
          xs = F.relu(self.fcs1(state))
          x = torch.cat((xs, action), dim=1)
          x = F.relu(self.fc2(x))
          return self.fc3(x)
  