gaussian_policy.py 文件源码

def compute_mean_and_var(self, x):
        h = x
        for layer in self.hidden_layers:
            h = self.nonlinearity(layer(h))
        mean = self.mean_layer(h)
        if self.bound_mean:
            mean = bound_by_tanh(mean, self.min_action, self.max_action)
        var = F.broadcast_to(F.softplus(self.var_layer(h)), mean.shape) + \
            self.min_var
        return mean, var