15355d6b1d45c157badd1c21157b529e817b7c3c,tf_agents/agents/reinforce/reinforce_agent.py,ReinforceAgent,_train,#ReinforceAgent#Any#Any#,121

Before Change

    )

    if self._summarize_grads_and_vars:
      with tf.name_scope("Variables/"):
        for var in self._actor_network.trainable_weights:
          tf.compat.v2.summary.histogram(
              name=var.name.replace(":", "_"),
              data=var,
              step=self.train_step_counter)

    return loss_info

  @eager_utils.future_in_eager_mode
  def _loss(self, time_steps, actions, returns, weights):

After Change

                            experience.observation)

    variables_to_train = self._actor_network.variables
    with tf.GradientTape() as tape:
      loss_info = self._loss(time_step,
                             experience.action,
                             tf.stop_gradient(returns),
                             weights=weights)
      tf.debugging.check_numerics(loss_info.loss, "Loss is inf or nan")
    grads = tape.gradient(loss_info.loss, variables_to_train)

    grads_and_vars = zip(grads, variables_to_train)
    if self._gradient_clipping:

Instances