Move some common logic to buffer class

ml-agents/mlagents/trainers/buffer.py

             for _key in self.keys():
                 self[_key] = self[_key][current_length - max_length :]
 
+    def resequence_and_append(
+        self,
+        target_buffer: "AgentBuffer",
+        key_list: List[str] = None,
+        batch_size: int = None,
+        training_length: int = None,
+    ) -> None:
+        """
+        Takes in a batch size and training length (sequence length), and appends this AgentBuffer to target_buffer
+        properly padded for LSTM use. Optionally, use key_list to restrict which fields are inserted into the new
+        buffer.
+        :param target_buffer: The buffer which to append the samples to.
+        :param key_list: The fields that must be added. If None: all fields will be appended.
+        :param batch_size: The number of elements that must be appended. If None: All of them will be.
+        :param training_length: The length of the samples that must be appended. If None: only takes one element.
+        """
+        if key_list is None:
+            key_list = list(self.keys())
+        if not self.check_length(key_list):
+            raise BufferException(
+                "The length of the fields {0} were not of same length".format(key_list)
+            )
+        for field_key in key_list:
+            target_buffer[field_key].extend(
+                self[field_key].get_batch(
+                    batch_size=batch_size, training_length=training_length
+                )
+            )
+
     @property
     def num_experiences(self) -> int:
         """

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

         agent_buffer_trajectory["advantages"].set(global_advantages)
         agent_buffer_trajectory["discounted_returns"].set(global_returns)
         # Append to update buffer
-        key_list = agent_buffer_trajectory.keys()
-        for field_key in key_list:
-            self.update_buffer[field_key].extend(
-                agent_buffer_trajectory[field_key].get_batch(
-                    batch_size=None, training_length=self.policy.sequence_length
-                )
-            )
+        agent_buffer_trajectory.resequence_and_append(
+            self.update_buffer, training_length=self.policy.sequence_length
+        )
 
         if trajectory.steps[-1].done:
             self.stats["Environment/Episode Length"].append(

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

             agent_buffer_trajectory["done"][-1] = False
 
         # Append to update buffer
-        key_list = agent_buffer_trajectory.keys()
-        for field_key in key_list:
-            self.update_buffer[field_key].extend(
-                agent_buffer_trajectory[field_key].get_batch(
-                    batch_size=None, training_length=self.policy.sequence_length
-                )
-            )
+        agent_buffer_trajectory.resequence_and_append(
+            self.update_buffer, training_length=self.policy.sequence_length
+        )
 
         if trajectory.steps[-1].done:
             self.stats["Environment/Episode Length"].append(