gaussian_lstm_policy.py 文件源码

def dist_info_sym(self, obs_var, state_info_vars):
        n_batches = tf.shape(obs_var)[0]
        n_steps = tf.shape(obs_var)[1]
        obs_var = tf.reshape(obs_var, tf.pack([n_batches, n_steps, -1]))
        if self.state_include_action:
            prev_action_var = state_info_vars["prev_action"]
            all_input_var = tf.concat(2, [obs_var, prev_action_var])
        else:
            all_input_var = obs_var
        if self.feature_network is None:
            means, log_stds = L.get_output(
                [self.mean_network.output_layer, self.l_log_std],
                {self.l_input: all_input_var}
            )
        else:
            flat_input_var = tf.reshape(all_input_var, (-1, self.input_dim))
            means, log_stds = L.get_output(
                [self.mean_network.output_layer, self.l_log_std],
                {self.l_input: all_input_var, self.feature_network.input_layer: flat_input_var}
            )
        return dict(mean=means, log_std=log_stds)