dueling_dqn.py 文件源码

def __call__(self, x):
        h = x
        for l in self.conv_layers:
            h = self.activation(l(h))

        # Advantage
        batch_size = x.shape[0]
        ya = self.a_stream(h)
        mean = F.reshape(
            F.sum(ya, axis=1) / self.n_actions, (batch_size, 1))
        ya, mean = F.broadcast(ya, mean)
        ya -= mean

        # State value
        ys = self.v_stream(h)

        ya, ys = F.broadcast(ya, ys)
        q = ya + ys
        return action_value.DiscreteActionValue(q)