network.py 文件源码

def _init_loss(cls, config, q, expected_q, actions, reg_loss=None,
                 summaries=None):
    """
    Setup the loss function and apply regularization is provided.

    @return: loss_op
    """
    q_masked = tf.reduce_sum(tf.mul(q, actions), reduction_indices=[1])
    loss = tf.reduce_mean(tf.squared_difference(q_masked, expected_q))
    if reg_loss is not None:
      loss += config.reg_param * reg_loss

    if summaries is not None:
      summaries.append(tf.scalar_summary('loss', loss))

    return loss