a049ecb81701dd61744cbbf5922c4dc2ee6542fa,ch07/lib/dqn_model.py,NoisyFactorizedLinear,__init__,#NoisyFactorizedLinear#Any#Any#Any#Any#,40

Before Change

    def __init__(self, in_features, out_features, sigma_zero=0.4, bias=True):
        super(NoisyFactorizedLinear, self).__init__(in_features, out_features, bias=bias)
        sigma_init = sigma_zero / math.sqrt(in_features)
        self.sigma_weight = nn.Parameter(torch.Tensor(out_features, in_features).fill_(sigma_init))
        self.register_buffer("epsilon_input", torch.zeros(1, in_features))
        self.register_buffer("epsilon_output", torch.zeros(out_features, 1))
        if bias:
            self.sigma_bias = nn.Parameter(torch.Tensor(out_features).fill_(sigma_init))

    def forward(self, input):
        torch.randn(self.epsilon_input.size(), out=self.epsilon_input)
        torch.randn(self.epsilon_output.size(), out=self.epsilon_output)

After Change

    def __init__(self, input_shape, n_actions, noisy_net=False, writer=None):
        super(DQN, self).__init__()

        self.conv = nn.Sequential(
            nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, stride=1),
            nn.ReLU()
        )

        OutLayer = NoisyLinear if noisy_net else nn.Linear

        conv_out_size = self._get_conv_out(input_shape)

Instances