Hands-On Reinforcement Learning for Games

The DQN class

Previously, we saw how the DQN class was used to construct the neural network model that we will use to learn the TD loss function. Reopen exercise Chapter_6_DQN.py again to review the construction of the DQN class:

1. The entire code for the DQN class is as follows:

class DQN(nn.Module):  def __init__(self, num_inputs, num_actions):    super(DQN, self).__init__()    self.layers = nn.Sequential(      nn.Linear(env.observation_space.shape[0], 128),      nn.ReLU(),      nn.Linear(128, 128),      nn.ReLU(),      nn.Linear(128, env.action_space.n))  def forward(self, x):    return self.layers(x)  def act(self, state, epsilon):    if random.random() > epsilon:      state   = autograd.Variable(torch.FloatTensor(state).unsqueeze(0),        volatile=True)      q_value = self.forward(state) ...