class DQN([nn.Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")):

    def __init__(self, n_observations, n_actions):
        super([DQN](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module"), self).__init__()
        self.layer1 = [nn.Linear](https://docs.pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear "torch.nn.Linear")(n_observations, 128)
        self.layer2 = [nn.Linear](https://docs.pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear "torch.nn.Linear")(128, 128)
        self.layer3 = [nn.Linear](https://docs.pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear "torch.nn.Linear")(128, n_actions)

    # Called with either one element to determine next action, or a batch
    # during optimization. Returns tensor([[left0exp,right0exp]...]).
    def forward(self, x):
        x = [F.relu](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.relu.html#torch.nn.functional.relu "torch.nn.functional.relu")(self.layer1(x))
        x = [F.relu](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.relu.html#torch.nn.functional.relu "torch.nn.functional.relu")(self.layer2(x))
        return self.layer3(x)