[skip ci] updating sac

ml-agents/mlagents/trainers/sac/trainer.py

             )
         )
 
+    def should_still_train(self) -> bool:
+        """
+        Returns whether or not the trainer should train. A Trainer could
+        stop training if it wasn't training to begin with, or if max_steps
+        is reached.
+        """
+        return self.is_training and self.steps_per_update * self.update_steps <= self.get_max_steps
+
-        super()._process_trajectory(trajectory)
+        # super()._process_trajectory(trajectory)
+        self._maybe_write_summary(self.get_step + int(self.steps_per_update))
+        self._maybe_save_model(self.get_step + int(self.steps_per_update))
+        self._increment_step(self.hyperparameters.buffer_size, self.brain_name)
+
         last_step = trajectory.steps[-1]
         agent_id = trajectory.agent_id  # All the agents should have the same ID
 
         """
         has_updated = False
         self.cumulative_returns_since_policy_update.clear()
-        self._maybe_write_summary(self.get_step + int(self.steps_per_update))
-        self._maybe_save_model(self.get_step + int(self.steps_per_update))
-        self._increment_step(self.hyperparameters.buffer_size, self.brain_name)
-
         n_sequences = max(
             int(self.hyperparameters.batch_size / self.policy.sequence_length), 1
         )
             self.step - self.hyperparameters.buffer_init_steps
         ) / self.update_steps > self.steps_per_update:
+
             logger.debug("Updating SAC policy at step {}".format(self.step))
             buffer = self.update_buffer
             if self.update_buffer.num_experiences >= self.hyperparameters.batch_size: