model.py

"""Defines an Actor (Policy) Model."""
import torch
import torch.nn as nn
import torch.nn.functional as F


class QNetwork(nn.Module):
    """Actor (Policy) Model."""

    def __init__(self, state_size, action_size, seed, fc1_units=64, fc2_units=64):
        """Initialize parameters and build the model.

        Args:
            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 the first hidden layer
            fc2_units (int): Number of nodes in the second hidden layer
        """
        super(QNetwork, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(state_size, fc1_units)  # fully connected layer
        self.fc2 = nn.Linear(fc1_units, fc2_units)  # fully connected layer
        self.fc3 = nn.Linear(fc2_units, action_size)  # fully connected layer

    def forward(self, state):
        """Build a network that maps state to action values.

        Args:
            state (array_like): input state
        Returns:
            action values (array_like): the output action values
        """
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        return self.fc3(x)