Revert buffer for now

ml-agents/mlagents/trainers/buffer.py

         if current_length > max_length:
             for _key in self.keys():
                 self[_key] = self[_key][current_length - max_length :]
+
+
+class AgentProcessorBuffer(dict):
+    """
+    AgentProcessorBuffer contains a dictionary of AgentBuffer. The AgentBuffers are indexed by agent_id.
+    Buffer also contains an update_buffer that corresponds to the buffer used when updating the model.
+    """
+
+    def __str__(self):
+        return "local_buffers :\n{0}".format(
+            "\n".join(["\tagent {0} :{1}".format(k, str(self[k])) for k in self.keys()])
+        )
+
+    def __getitem__(self, key):
+        if key not in self.keys():
+            self[key] = AgentBuffer()
+        return super().__getitem__(key)
+
+    def reset_local_buffers(self) -> None:
+        """
+        Resets all the local local_buffers
+        """
+        agent_ids = list(self.keys())
+        for k in agent_ids:
+            self[k].reset_agent()
+
+    def append_to_update_buffer(
+        self,
+        update_buffer: AgentBuffer,
+        agent_id: str,
+        key_list: List[str] = None,
+        batch_size: int = None,
+        training_length: int = None,
+    ) -> None:
+        """
+        Appends the buffer of an agent to the update buffer.
+        :param agent_id: The id of the agent which data will be appended
+        :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 = self[agent_id].keys()
+        if not self[agent_id].check_length(key_list):
+            raise BufferException(
+                "The length of the fields {0} for agent {1} where not of same length".format(
+                    key_list, agent_id
+                )
+            )
+        for field_key in key_list:
+            update_buffer[field_key].extend(
+                self[agent_id][field_key].get_batch(
+                    batch_size=batch_size, training_length=training_length
+                )
+            )
+
+    def append_all_agent_batch_to_update_buffer(
+        self,
+        update_buffer: AgentBuffer,
+        key_list: List[str] = None,
+        batch_size: int = None,
+        training_length: int = None,
+    ) -> None:
+        """
+        Appends the buffer of all agents to the update buffer.
+        :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.
+        """
+        for agent_id in self.keys():
+            self.append_to_update_buffer(
+                update_buffer, agent_id, key_list, batch_size, training_length
+            )